diff --git a/Makefile b/Makefile
index a7e6d17..1c9dcd4 100644
--- a/Makefile
+++ b/Makefile
@@ -50,7 +50,7 @@ jupyter:
 
 run-notebooks:
 	@echo "Running notebooks"
-	@cd docs && poetry run python run_notebooks.py
+	poetry run python docs/run_notebooks.py
 
 test:
 	@echo "Running tests"
diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb
index 31a02f5..3232c2c 100644
--- a/docs/getting_started.ipynb
+++ b/docs/getting_started.ipynb
@@ -9,11 +9,17 @@
      "end_time": "2024-04-27T10:05:25.636065Z",
      "start_time": "2024-04-27T10:05:25.632244Z"
     },
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:54:51.218123Z",
+     "iopub.status.busy": "2025-01-06T14:54:51.217792Z",
+     "iopub.status.idle": "2025-01-06T14:54:51.226882Z",
+     "shell.execute_reply": "2025-01-06T14:54:51.225419Z"
+    },
     "papermill": {
-     "duration": 0.023706,
-     "end_time": "2024-01-31T17:49:07.385771",
+     "duration": 0.017633,
+     "end_time": "2025-01-06T14:54:51.228549",
      "exception": false,
-     "start_time": "2024-01-31T17:49:07.362065",
+     "start_time": "2025-01-06T14:54:51.210916",
      "status": "completed"
     },
     "tags": []
@@ -34,25 +40,22 @@
      "end_time": "2024-04-27T10:05:29.654652Z",
      "start_time": "2024-04-27T10:05:25.635956Z"
     },
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:54:51.239921Z",
+     "iopub.status.busy": "2025-01-06T14:54:51.239602Z",
+     "iopub.status.idle": "2025-01-06T14:55:01.447783Z",
+     "shell.execute_reply": "2025-01-06T14:55:01.446539Z"
+    },
     "papermill": {
-     "duration": 2.47651,
-     "end_time": "2024-01-31T17:49:09.867453",
+     "duration": 10.215408,
+     "end_time": "2025-01-06T14:55:01.449362",
      "exception": false,
-     "start_time": "2024-01-31T17:49:07.390943",
+     "start_time": "2025-01-06T14:54:51.233954",
      "status": "completed"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import ontime as on\n",
     "import pandas as pd"
@@ -63,10 +66,10 @@
    "id": "9f94ac2b-bc8a-4757-affb-6e570a024804",
    "metadata": {
     "papermill": {
-     "duration": 0.002608,
-     "end_time": "2024-01-31T17:49:09.878216",
+     "duration": 0.003865,
+     "end_time": "2025-01-06T14:55:01.457052",
      "exception": false,
-     "start_time": "2024-01-31T17:49:09.875608",
+     "start_time": "2025-01-06T14:55:01.453187",
      "status": "completed"
     },
     "tags": []
@@ -86,10 +89,10 @@
    "id": "c0271c7d-d9b4-414e-b7be-83adeafcc741",
    "metadata": {
     "papermill": {
-     "duration": 0.002602,
-     "end_time": "2024-01-31T17:49:09.893863",
+     "duration": 0.00426,
+     "end_time": "2025-01-06T14:55:01.465369",
      "exception": false,
-     "start_time": "2024-01-31T17:49:09.891261",
+     "start_time": "2025-01-06T14:55:01.461109",
      "status": "completed"
     },
     "tags": []
@@ -119,11 +122,17 @@
      "end_time": "2024-04-27T10:05:29.670567Z",
      "start_time": "2024-04-27T10:05:29.659699Z"
     },
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:01.474817Z",
+     "iopub.status.busy": "2025-01-06T14:55:01.474325Z",
+     "iopub.status.idle": "2025-01-06T14:55:01.502154Z",
+     "shell.execute_reply": "2025-01-06T14:55:01.501007Z"
+    },
     "papermill": {
-     "duration": 0.012602,
-     "end_time": "2024-01-31T17:49:09.909081",
+     "duration": 0.034002,
+     "end_time": "2025-01-06T14:55:01.503475",
      "exception": false,
-     "start_time": "2024-01-31T17:49:09.896479",
+     "start_time": "2025-01-06T14:55:01.469473",
      "status": "completed"
     },
     "tags": []
@@ -162,13 +171,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -213,7 +223,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -221,7 +231,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -233,6 +245,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -254,7 +270,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -265,7 +281,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -337,15 +353,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -495,50 +511,50 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt;\n",
-       "array([[[-1.37205979]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt; Size: 40B\n",
+       "array([[[ 0.8042713 ]],\n",
        "\n",
-       "       [[-0.65182852]],\n",
+       "       [[ 1.58685749]],\n",
        "\n",
-       "       [[ 0.29932213]],\n",
+       "       [[ 0.88496745]],\n",
        "\n",
-       "       [[-0.94618418]],\n",
+       "       [[ 1.07890811]],\n",
        "\n",
-       "       [[-0.67047005]]])\n",
+       "       [[-0.41554812]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 40B 2023-01-01 2023-01-02 ... 2023-01-05\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ba9522f2-524f-4870-8df9-eb0d0614d1a9' class='xr-array-in' type='checkbox' checked><label for='section-ba9522f2-524f-4870-8df9-eb0d0614d1a9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.372 -0.6518 0.2993 -0.9462 -0.6705</span></div><div class='xr-array-data'><pre>array([[[-1.37205979]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-46a85291-ef70-47fd-822a-8b91b53a7067' class='xr-array-in' type='checkbox' checked><label for='section-46a85291-ef70-47fd-822a-8b91b53a7067' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.8043 1.587 0.885 1.079 -0.4155</span></div><div class='xr-array-data'><pre>array([[[ 0.8042713 ]],\n",
        "\n",
-       "       [[-0.65182852]],\n",
+       "       [[ 1.58685749]],\n",
        "\n",
-       "       [[ 0.29932213]],\n",
+       "       [[ 0.88496745]],\n",
        "\n",
-       "       [[-0.94618418]],\n",
+       "       [[ 1.07890811]],\n",
        "\n",
-       "       [[-0.67047005]]])</pre></div></div></li><li class='xr-section-item'><input id='section-815d90c1-1a57-4a09-9d75-bb15918d7279' class='xr-section-summary-in' type='checkbox'  checked><label for='section-815d90c1-1a57-4a09-9d75-bb15918d7279' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-01 ... 2023-01-05</div><input id='attrs-67472ae5-4119-40a4-9360-f55828989f13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67472ae5-4119-40a4-9360-f55828989f13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec16e45-f661-4c56-b556-5f692be423d5' class='xr-var-data-in' type='checkbox'><label for='data-4ec16e45-f661-4c56-b556-5f692be423d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[-0.41554812]]])</pre></div></div></li><li class='xr-section-item'><input id='section-a6a65f57-621e-48f6-9111-69d8e0cfa3de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a6a65f57-621e-48f6-9111-69d8e0cfa3de' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-01 ... 2023-01-05</div><input id='attrs-4ecd5092-0b9c-427d-b36d-930508a71e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ecd5092-0b9c-427d-b36d-930508a71e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04f29901-dee8-4188-9abf-d80de9e743f7' class='xr-var-data-in' type='checkbox'><label for='data-04f29901-dee8-4188-9abf-d80de9e743f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-03T00:00:00.000000000&#x27;, &#x27;2023-01-04T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-60da1830-b873-40c8-8af7-0f694b427890' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60da1830-b873-40c8-8af7-0f694b427890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44accb6f-c973-44b1-8ffb-dd0c70558f47' class='xr-var-data-in' type='checkbox'><label for='data-44accb6f-c973-44b1-8ffb-dd0c70558f47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdeef670-72ec-4d48-aa20-19c0589bed0e' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdeef670-72ec-4d48-aa20-19c0589bed0e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b4ba404-02a8-4208-a8bd-137ffd50e231' class='xr-index-data-in' type='checkbox'/><label for='index-4b4ba404-02a8-4208-a8bd-137ffd50e231' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-01&#x27;, &#x27;2023-01-02&#x27;, &#x27;2023-01-03&#x27;, &#x27;2023-01-04&#x27;,\n",
+       "       &#x27;2023-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-1ac0f1e3-1f6d-4858-873f-344b4bc4aea2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ac0f1e3-1f6d-4858-873f-344b4bc4aea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a58cdfe-b1d9-4bd4-92cc-9a5d1f58fb9e' class='xr-var-data-in' type='checkbox'><label for='data-6a58cdfe-b1d9-4bd4-92cc-9a5d1f58fb9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e2c4b3c-8e7a-4d96-9e0d-46143c6bac95' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e2c4b3c-8e7a-4d96-9e0d-46143c6bac95' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-600d36ec-094a-426d-bb58-8fe823f4fc98' class='xr-index-data-in' type='checkbox'/><label for='index-600d36ec-094a-426d-bb58-8fe823f4fc98' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-01&#x27;, &#x27;2023-01-02&#x27;, &#x27;2023-01-03&#x27;, &#x27;2023-01-04&#x27;,\n",
        "               &#x27;2023-01-05&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25196440-7c80-4392-ba4c-b30b290a72cd' class='xr-index-data-in' type='checkbox'/><label for='index-25196440-7c80-4392-ba4c-b30b290a72cd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b0d82af-1baf-4b9a-b0dc-bccc0ef83a03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b0d82af-1baf-4b9a-b0dc-bccc0ef83a03' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-25157275-dad4-4105-b66c-a8045a926366' class='xr-index-data-in' type='checkbox'/><label for='index-25157275-dad4-4105-b66c-a8045a926366' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2f186a5-171f-4da7-8baa-b9cf775516b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2f186a5-171f-4da7-8baa-b9cf775516b4' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-1.37205979]],\n",
+       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)> Size: 40B\n",
+       "array([[[ 0.8042713 ]],\n",
        "\n",
-       "       [[-0.65182852]],\n",
+       "       [[ 1.58685749]],\n",
        "\n",
-       "       [[ 0.29932213]],\n",
+       "       [[ 0.88496745]],\n",
        "\n",
-       "       [[-0.94618418]],\n",
+       "       [[ 1.07890811]],\n",
        "\n",
-       "       [[-0.67047005]]])\n",
+       "       [[-0.41554812]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 40B 2023-01-01 2023-01-02 ... 2023-01-05\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -564,11 +580,17 @@
      "end_time": "2024-04-27T10:05:32.472025Z",
      "start_time": "2024-04-27T10:05:32.234524Z"
     },
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:01.514035Z",
+     "iopub.status.busy": "2025-01-06T14:55:01.513737Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.324878Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.323545Z"
+    },
     "papermill": {
-     "duration": 0.181859,
-     "end_time": "2024-01-31T17:49:10.093904",
+     "duration": 0.818872,
+     "end_time": "2025-01-06T14:55:02.326710",
      "exception": false,
-     "start_time": "2024-01-31T17:49:09.912045",
+     "start_time": "2025-01-06T14:55:01.507838",
      "status": "completed"
     },
     "tags": []
@@ -576,17 +598,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-d92b05a1b1c745638a9b309e28d0847a.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-d92b05a1b1c745638a9b309e28d0847a.vega-embed details,\n",
+       "  #altair-viz-d92b05a1b1c745638a9b309e28d0847a.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-d92b05a1b1c745638a9b309e28d0847a\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-d92b05a1b1c745638a9b309e28d0847a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-d92b05a1b1c745638a9b309e28d0847a\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2023-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.8042713021598082}, {\"time\": \"2023-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.58685749495609}, {\"time\": \"2023-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.8849674548970128}, {\"time\": \"2023-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0789081095673738}, {\"time\": \"2023-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.41554812285693465}, {\"time\": \"2023-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.20409365321770112}, {\"time\": \"2023-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.26709737543046475}, {\"time\": \"2023-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.6125244095268069}, {\"time\": \"2023-01-09T00:00:00.000\", 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\"random_walk\", \"value\": 18.889330578973258}, {\"time\": \"2023-12-23T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 19.372128609748913}, {\"time\": \"2023-12-24T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 20.76833306936785}, {\"time\": \"2023-12-25T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 22.10402195029223}, {\"time\": \"2023-12-26T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 21.92051375150228}, {\"time\": \"2023-12-27T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 21.476265109769223}, {\"time\": \"2023-12-28T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 23.017807099503273}, {\"time\": \"2023-12-29T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 21.928598335260016}, {\"time\": \"2023-12-30T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 22.432638367867533}, {\"time\": \"2023-12-31T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 23.826616161848637}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": -7.564921296912824, \"max\": 25.13487149805414}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"random_walk\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": true, \"gridScale\": \"y\", \"minExtent\": 0, \"ticks\": false, \"orient\": \"bottom\", \"maxExtent\": 0, \"zindex\": 0, \"domain\": false, \"labels\": false, \"aria\": false}, {\"scale\": \"y\", \"labels\": false, \"aria\": false, \"maxExtent\": 0, \"orient\": \"left\", \"grid\": true, \"minExtent\": 0, \"ticks\": false, \"gridScale\": \"x\", \"domain\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0}, {\"scale\": \"x\", \"title\": \"time\", \"orient\": \"bottom\", \"labelOverlap\": true, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"labelFlush\": true}, {\"scale\": \"y\", \"title\": \"value\", \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"labelOverlap\": true, \"zindex\": 0, \"grid\": false}], \"width\": 400, \"height\": 200, \"style\": \"cell\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"background\": \"white\", \"padding\": 5}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts.plot();"
+    "ts.plot()"
    ]
   },
   {
@@ -594,10 +686,10 @@
    "id": "2487b267-ec4a-49bc-9bda-33e4bc39194c",
    "metadata": {
     "papermill": {
-     "duration": 0.004476,
-     "end_time": "2024-01-31T17:49:10.107412",
+     "duration": 0.006113,
+     "end_time": "2025-01-06T14:55:02.337993",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.102936",
+     "start_time": "2025-01-06T14:55:02.331880",
      "status": "completed"
     },
     "tags": []
@@ -611,10 +703,10 @@
    "id": "0904039a-4cb4-4795-9656-8c48a919dfee",
    "metadata": {
     "papermill": {
-     "duration": 0.003459,
-     "end_time": "2024-01-31T17:49:10.125625",
+     "duration": 0.005795,
+     "end_time": "2025-01-06T14:55:02.349321",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.122166",
+     "start_time": "2025-01-06T14:55:02.343526",
      "status": "completed"
     },
     "tags": []
@@ -628,11 +720,17 @@
    "execution_count": 5,
    "id": "4c906526-20c4-47b5-8023-8216c6af18d1",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.361266Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.360919Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.494547Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.493325Z"
+    },
     "papermill": {
-     "duration": 0.095086,
-     "end_time": "2024-01-31T17:49:10.252726",
+     "duration": 0.142101,
+     "end_time": "2025-01-06T14:55:02.496411",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.157640",
+     "start_time": "2025-01-06T14:55:02.354310",
      "status": "completed"
     },
     "tags": []
@@ -648,11 +746,17 @@
    "execution_count": 6,
    "id": "15724cb7-d0d6-40ab-a240-57df7e8f3c21",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.509380Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.509048Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.536066Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.534966Z"
+    },
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-     "duration": 0.017408,
-     "end_time": "2024-01-31T17:49:10.273465",
+     "duration": 0.035638,
+     "end_time": "2025-01-06T14:55:02.538050",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.256057",
+     "start_time": "2025-01-06T14:55:02.502412",
      "status": "completed"
     },
     "tags": []
@@ -670,11 +774,17 @@
    "execution_count": 7,
    "id": "401cf66e-f0eb-48e0-ac7c-adf2bef92a3c",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.552242Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.551909Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.561579Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.560224Z"
+    },
     "papermill": {
-     "duration": 0.007854,
-     "end_time": "2024-01-31T17:49:10.284420",
+     "duration": 0.018558,
+     "end_time": "2025-01-06T14:55:02.563072",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.276566",
+     "start_time": "2025-01-06T14:55:02.544514",
      "status": "completed"
     },
     "tags": []
@@ -689,11 +799,17 @@
    "execution_count": 8,
    "id": "3013d9e8-d7cd-4caf-be3f-607eab84647e",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.575058Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.574739Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.587100Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.586052Z"
+    },
     "papermill": {
-     "duration": 0.0091,
-     "end_time": "2024-01-31T17:49:10.296540",
+     "duration": 0.020382,
+     "end_time": "2025-01-06T14:55:02.588677",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.287440",
+     "start_time": "2025-01-06T14:55:02.568295",
      "status": "completed"
     },
     "tags": []
@@ -709,10 +825,10 @@
    "id": "a1a81ffb-dca7-4ee5-93e9-836223ae6956",
    "metadata": {
     "papermill": {
-     "duration": 0.003024,
-     "end_time": "2024-01-31T17:49:10.302556",
+     "duration": 0.005689,
+     "end_time": "2025-01-06T14:55:02.599846",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.299532",
+     "start_time": "2025-01-06T14:55:02.594157",
      "status": "completed"
     },
     "tags": []
@@ -726,10 +842,10 @@
    "id": "adb031b1-74bc-4ed2-ae54-bc16681f0363",
    "metadata": {
     "papermill": {
-     "duration": 0.002959,
-     "end_time": "2024-01-31T17:49:10.308461",
+     "duration": 0.005222,
+     "end_time": "2025-01-06T14:55:02.610856",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.305502",
+     "start_time": "2025-01-06T14:55:02.605634",
      "status": "completed"
     },
     "tags": []
@@ -740,14 +856,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "79fd0990-dac4-461e-98b0-29f1e579b11a",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.623734Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.623401Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.631002Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.629810Z"
+    },
     "papermill": {
-     "duration": 0.007087,
-     "end_time": "2024-01-31T17:49:10.318529",
+     "duration": 0.016223,
+     "end_time": "2025-01-06T14:55:02.632157",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.311442",
+     "start_time": "2025-01-06T14:55:02.615934",
      "status": "completed"
     },
     "tags": []
@@ -762,10 +884,10 @@
    "id": "0641d9e6-c5b8-49a1-a3db-9df1927d62be",
    "metadata": {
     "papermill": {
-     "duration": 0.003068,
-     "end_time": "2024-01-31T17:49:10.324885",
+     "duration": 0.005184,
+     "end_time": "2025-01-06T14:55:02.642940",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.321817",
+     "start_time": "2025-01-06T14:55:02.637756",
      "status": "completed"
     },
     "tags": []
@@ -779,10 +901,10 @@
    "id": "2982c551-1607-40c8-911d-154a3493e4b1",
    "metadata": {
     "papermill": {
-     "duration": 0.003102,
-     "end_time": "2024-01-31T17:49:10.331104",
+     "duration": 0.005269,
+     "end_time": "2025-01-06T14:55:02.655220",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.328002",
+     "start_time": "2025-01-06T14:55:02.649951",
      "status": "completed"
     },
     "tags": []
@@ -796,10 +918,10 @@
    "id": "f7fe172e-588a-4f7e-aa0e-1cdc07dd8aca",
    "metadata": {
     "papermill": {
-     "duration": 0.002994,
-     "end_time": "2024-01-31T17:49:10.337191",
+     "duration": 0.006665,
+     "end_time": "2025-01-06T14:55:02.713726",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.334197",
+     "start_time": "2025-01-06T14:55:02.707061",
      "status": "completed"
     },
     "tags": []
@@ -810,14 +932,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "ad40696c-c5fa-4b5f-8f49-92c4fc0a9941",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.728647Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.728270Z",
+     "iopub.status.idle": "2025-01-06T14:55:02.733718Z",
+     "shell.execute_reply": "2025-01-06T14:55:02.732394Z"
+    },
     "papermill": {
-     "duration": 0.005962,
-     "end_time": "2024-01-31T17:49:10.346226",
+     "duration": 0.014858,
+     "end_time": "2025-01-06T14:55:02.735410",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.340264",
+     "start_time": "2025-01-06T14:55:02.720552",
      "status": "completed"
     },
     "tags": []
@@ -832,10 +960,10 @@
    "id": "0a304607-7ccb-4d2b-9796-dd80bce76b6c",
    "metadata": {
     "papermill": {
-     "duration": 0.003121,
-     "end_time": "2024-01-31T17:49:10.352586",
+     "duration": 0.007953,
+     "end_time": "2025-01-06T14:55:02.750564",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.349465",
+     "start_time": "2025-01-06T14:55:02.742611",
      "status": "completed"
     },
     "tags": []
@@ -846,34 +974,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "bac9e224-c12a-4d7a-b7ca-db2a9acff278",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:02.766674Z",
+     "iopub.status.busy": "2025-01-06T14:55:02.765558Z",
+     "iopub.status.idle": "2025-01-06T14:55:03.265891Z",
+     "shell.execute_reply": "2025-01-06T14:55:03.264434Z"
+    },
     "papermill": {
-     "duration": 0.136216,
-     "end_time": "2024-01-31T17:49:10.491926",
+     "duration": 0.510698,
+     "end_time": "2025-01-06T14:55:03.267737",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.355710",
+     "start_time": "2025-01-06T14:55:02.757039",
      "status": "completed"
     },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n",
-    "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)"
+    "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN).rename({\"value\": \"week_mean\"})\n",
+    "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN).rename({\"value\": \"week_median\"}) # variable is renamed cause error raises when set to \"value\" "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "e8474db3-5e1e-4d08-bdc1-30f88bfd5f28",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:03.284113Z",
+     "iopub.status.busy": "2025-01-06T14:55:03.283294Z",
+     "iopub.status.idle": "2025-01-06T14:55:03.378595Z",
+     "shell.execute_reply": "2025-01-06T14:55:03.377389Z"
+    },
     "papermill": {
-     "duration": 0.136824,
-     "end_time": "2024-01-31T17:49:10.631997",
+     "duration": 0.106033,
+     "end_time": "2025-01-06T14:55:03.380586",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.495173",
+     "start_time": "2025-01-06T14:55:03.274553",
      "status": "completed"
     },
     "tags": []
@@ -881,18 +1021,92 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-26bdc6a587724645b7e04855e79807f6.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-26bdc6a587724645b7e04855e79807f6.vega-embed details,\n",
+       "  #altair-viz-26bdc6a587724645b7e04855e79807f6.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-26bdc6a587724645b7e04855e79807f6\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-26bdc6a587724645b7e04855e79807f6\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-26bdc6a587724645b7e04855e79807f6\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-01-01T00:00:00.000\", \"variable\": \"week_mean\", \"value\": 231.22641509433961}, {\"time\": \"2015-01-01T01:00:00.000\", \"variable\": \"week_mean\", \"value\": 202.22641509433961}, {\"time\": \"2015-01-01T02:00:00.000\", \"variable\": \"week_mean\", \"value\": 172.88679245283018}, {\"time\": \"2015-01-01T03:00:00.000\", \"variable\": \"week_mean\", \"value\": 148.8679245283019}, {\"time\": \"2015-01-01T04:00:00.000\", \"variable\": \"week_mean\", \"value\": 117.58490566037736}, {\"time\": \"2015-01-01T05:00:00.000\", \"variable\": \"week_mean\", \"value\": 130.43396226415095}, {\"time\": \"2015-01-01T06:00:00.000\", \"variable\": \"week_mean\", \"value\": 357.1320754716981}, {\"time\": \"2015-01-01T07:00:00.000\", \"variable\": \"week_mean\", \"value\": 1111.754716981132}, {\"time\": \"2015-01-01T08:00:00.000\", \"variable\": \"week_mean\", 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+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 12,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "week_mean.plot();\n",
-    "week_median.plot();"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, week_mean)\n",
+    "    .add(on.marks.line, week_median)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -900,10 +1114,10 @@
    "id": "3abbc2e7-a631-40b0-878c-36a552f95dc8",
    "metadata": {
     "papermill": {
-     "duration": 0.00339,
-     "end_time": "2024-01-31T17:49:10.639004",
+     "duration": 0.007541,
+     "end_time": "2025-01-06T14:55:03.395631",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.635614",
+     "start_time": "2025-01-06T14:55:03.388090",
      "status": "completed"
     },
     "tags": []
@@ -914,14 +1128,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "c775b740-fe78-4c0c-87fe-a51af3d42993",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:03.414483Z",
+     "iopub.status.busy": "2025-01-06T14:55:03.413617Z",
+     "iopub.status.idle": "2025-01-06T14:55:03.419310Z",
+     "shell.execute_reply": "2025-01-06T14:55:03.417690Z"
+    },
     "papermill": {
-     "duration": 0.006444,
-     "end_time": "2024-01-31T17:49:10.648812",
+     "duration": 0.018607,
+     "end_time": "2025-01-06T14:55:03.421196",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.642368",
+     "start_time": "2025-01-06T14:55:03.402589",
      "status": "completed"
     },
     "tags": []
@@ -933,14 +1153,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "a633dce3-27d8-4deb-bb0f-d40e3af96f8a",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:03.438597Z",
+     "iopub.status.busy": "2025-01-06T14:55:03.438209Z",
+     "iopub.status.idle": "2025-01-06T14:55:06.521349Z",
+     "shell.execute_reply": "2025-01-06T14:55:06.520059Z"
+    },
     "papermill": {
-     "duration": 1.179973,
-     "end_time": "2024-01-31T17:49:11.832154",
+     "duration": 3.095267,
+     "end_time": "2025-01-06T14:55:06.523602",
      "exception": false,
-     "start_time": "2024-01-31T17:49:10.652181",
+     "start_time": "2025-01-06T14:55:03.428335",
      "status": "completed"
     },
     "tags": []
@@ -949,10 +1175,10 @@
     {
      "data": {
       "text/plain": [
-       "<ontime.context.common.generic_predictor.GenericPredictor at 0x2b9906ec0>"
+       "<ontime.context.common.generic_predictor.GenericPredictor at 0x7f775011cdf0>"
       ]
      },
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@@ -966,10 +1192,10 @@
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    "metadata": {
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-     "end_time": "2024-01-31T17:49:11.839240",
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@@ -980,14 +1206,20 @@
   },
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    "cell_type": "code",
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+   "execution_count": 15,
    "id": "04dfe189-f736-4fe3-b27c-467c154b2c42",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:06.565871Z",
+     "iopub.status.busy": "2025-01-06T14:55:06.565370Z",
+     "iopub.status.idle": "2025-01-06T14:55:06.621667Z",
+     "shell.execute_reply": "2025-01-06T14:55:06.620024Z"
+    },
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-     "end_time": "2024-01-31T17:49:11.860424",
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@@ -999,14 +1231,20 @@
   },
   {
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+   "execution_count": 16,
    "id": "67da9001-1e2a-4578-9436-8db8151190dc",
    "metadata": {
+    "execution": {
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+     "iopub.status.idle": "2025-01-06T14:55:06.893283Z",
+     "shell.execute_reply": "2025-01-06T14:55:06.891342Z"
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-     "end_time": "2024-01-31T17:49:11.988975",
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@@ -1017,28 +1255,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-729059139fdd47e0a4225639737e3586.vega-embed {\n",
+       "  #altair-viz-9e896f22603b4c4f9439c15e5b0d3f44.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-729059139fdd47e0a4225639737e3586.vega-embed details,\n",
-       "  #altair-viz-729059139fdd47e0a4225639737e3586.vega-embed details summary {\n",
+       "  #altair-viz-9e896f22603b4c4f9439c15e5b0d3f44.vega-embed details,\n",
+       "  #altair-viz-9e896f22603b4c4f9439c15e5b0d3f44.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-729059139fdd47e0a4225639737e3586\"></div>\n",
+       "<div id=\"altair-viz-9e896f22603b4c4f9439c15e5b0d3f44\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-729059139fdd47e0a4225639737e3586\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-729059139fdd47e0a4225639737e3586\");\n",
+       "    if (outputDiv.id !== \"altair-viz-9e896f22603b4c4f9439c15e5b0d3f44\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-9e896f22603b4c4f9439c15e5b0d3f44\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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@@ -1071,28 +1310,36 @@
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        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
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\"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_2\", \"values\": [{\"time\": \"2015-09-13T13:00:00.000\", \"variable\": \"Truth\", \"value\": 4599.0}, {\"time\": \"2015-09-13T14:00:00.000\", \"variable\": \"Truth\", \"value\": 4261.0}, {\"time\": \"2015-09-13T15:00:00.000\", \"variable\": \"Truth\", \"value\": 3805.0}, {\"time\": \"2015-09-13T16:00:00.000\", \"variable\": \"Truth\", \"value\": 3118.0}, {\"time\": \"2015-09-13T17:00:00.000\", \"variable\": \"Truth\", \"value\": 1844.0}, {\"time\": \"2015-09-13T18:00:00.000\", \"variable\": \"Truth\", \"value\": 747.0}, {\"time\": \"2015-09-13T19:00:00.000\", \"variable\": \"Truth\", \"value\": 557.0}, {\"time\": \"2015-09-13T20:00:00.000\", \"variable\": \"Truth\", \"value\": 436.0}, {\"time\": \"2015-09-13T21:00:00.000\", \"variable\": \"Truth\", \"value\": 327.0}, {\"time\": \"2015-09-13T22:00:00.000\", \"variable\": \"Truth\", \"value\": 300.0}, {\"time\": \"2015-09-13T23:00:00.000\", \"variable\": 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{\"time\": \"2015-09-15T11:00:00.000\", \"variable\": \"Truth\", \"value\": 3498.0}, {\"time\": \"2015-09-15T12:00:00.000\", \"variable\": \"Truth\", \"value\": 3149.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"Training set\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"Prediction\"}]}, {\"name\": \"source_2_color_domain_variable_2\", \"values\": [{\"variable\": \"Truth\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": 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\"faceted_path_layer_1_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_2_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_2\", \"name\": \"faceted_path_layer_2_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_2_marks\", \"from\": {\"data\": \"faceted_path_layer_2_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"strokeDash\": {\"value\": [5, 2]}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"opacity\": {\"value\": 0.7}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}, {\"data\": \"source_2\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}, {\"data\": \"source_2\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}, {\"data\": \"source_2_color_domain_variable_2\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"gridScale\": \"y\", \"ticks\": false, \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"labels\": false, \"grid\": true, \"maxExtent\": 0, \"aria\": false, \"zindex\": 0, \"minExtent\": 0, \"domain\": false}, {\"scale\": \"y\", \"maxExtent\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"grid\": true, \"orient\": \"left\", \"gridScale\": \"x\", \"minExtent\": 0, \"domain\": false, \"ticks\": false, \"labels\": false, \"aria\": false}, {\"scale\": \"x\", \"orient\": \"bottom\", \"labelOverlap\": true, \"labelFlush\": true, \"grid\": false, \"title\": \"time\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0}, {\"scale\": \"y\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"grid\": false, \"orient\": \"left\", \"title\": \"value\"}], \"width\": 600, \"background\": \"white\", \"style\": \"cell\", \"padding\": 5, \"height\": 300, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}]}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
-       "alt.Chart(...)"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.prediction(train[-96:], pred, test[:48])"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, train[-96:].rename({\"generation solar\": \"Training set\"}))\n",
+    "    .add(on.marks.line, pred.rename({\"generation solar\": \"Prediction\"}))\n",
+    "    .add(on.marks.line, test[:48].rename({\"generation solar\": \"Truth\"}), type=\"dashed\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -1100,10 +1347,10 @@
    "id": "dc965c95-1b5c-43e4-9c4b-57ff73c275c1",
    "metadata": {
     "papermill": {
-     "duration": 0.003523,
-     "end_time": "2024-01-31T17:49:11.996207",
+     "duration": 0.011266,
+     "end_time": "2025-01-06T14:55:06.920028",
      "exception": false,
-     "start_time": "2024-01-31T17:49:11.992684",
+     "start_time": "2025-01-06T14:55:06.908762",
      "status": "completed"
     },
     "tags": []
@@ -1114,14 +1361,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "9751b373-97d3-45e9-9969-2b4ba224f815",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:06.944400Z",
+     "iopub.status.busy": "2025-01-06T14:55:06.943014Z",
+     "iopub.status.idle": "2025-01-06T14:55:06.950757Z",
+     "shell.execute_reply": "2025-01-06T14:55:06.948864Z"
+    },
     "papermill": {
-     "duration": 0.006672,
-     "end_time": "2024-01-31T17:49:12.006362",
+     "duration": 0.022549,
+     "end_time": "2025-01-06T14:55:06.953310",
      "exception": false,
-     "start_time": "2024-01-31T17:49:11.999690",
+     "start_time": "2025-01-06T14:55:06.930761",
      "status": "completed"
     },
     "tags": []
@@ -1133,14 +1386,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "89be0c48-0ab6-42b4-ab64-611b95d2a76f",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:06.976981Z",
+     "iopub.status.busy": "2025-01-06T14:55:06.976465Z",
+     "iopub.status.idle": "2025-01-06T14:55:10.229866Z",
+     "shell.execute_reply": "2025-01-06T14:55:10.227977Z"
+    },
     "papermill": {
-     "duration": 1.134506,
-     "end_time": "2024-01-31T17:49:13.144278",
+     "duration": 3.267734,
+     "end_time": "2025-01-06T14:55:10.232234",
      "exception": false,
-     "start_time": "2024-01-31T17:49:12.009772",
+     "start_time": "2025-01-06T14:55:06.964500",
      "status": "completed"
     },
     "tags": []
@@ -1149,10 +1408,10 @@
     {
      "data": {
       "text/plain": [
-       "<ontime.context.common.generic_detector.GenericDetector at 0x2b98d03a0>"
+       "<ontime.context.common.generic_detector.GenericDetector at 0x7f775011e980>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1166,10 +1425,10 @@
    "id": "06be738b-8bfd-4c95-8dec-ed52803e5ff9",
    "metadata": {
     "papermill": {
-     "duration": 0.003534,
-     "end_time": "2024-01-31T17:49:13.151717",
+     "duration": 0.010556,
+     "end_time": "2025-01-06T14:55:10.253171",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.148183",
+     "start_time": "2025-01-06T14:55:10.242615",
      "status": "completed"
     },
     "tags": []
@@ -1180,14 +1439,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "4650f34a-9cdb-4ea6-9dfe-2c66109b2627",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:10.276112Z",
+     "iopub.status.busy": "2025-01-06T14:55:10.275619Z",
+     "iopub.status.idle": "2025-01-06T14:55:10.303002Z",
+     "shell.execute_reply": "2025-01-06T14:55:10.301094Z"
+    },
     "papermill": {
-     "duration": 0.012201,
-     "end_time": "2024-01-31T17:49:13.167405",
+     "duration": 0.042027,
+     "end_time": "2025-01-06T14:55:10.305378",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.155204",
+     "start_time": "2025-01-06T14:55:10.263351",
      "status": "completed"
     },
     "tags": []
@@ -1199,14 +1464,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "49e09caa-37f1-4201-b79a-4d1d65d86a8c",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:10.329853Z",
+     "iopub.status.busy": "2025-01-06T14:55:10.329086Z",
+     "iopub.status.idle": "2025-01-06T14:55:10.484409Z",
+     "shell.execute_reply": "2025-01-06T14:55:10.480765Z"
+    },
     "papermill": {
-     "duration": 0.03959,
-     "end_time": "2024-01-31T17:49:13.210537",
+     "duration": 0.171051,
+     "end_time": "2025-01-06T14:55:10.486803",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.170947",
+     "start_time": "2025-01-06T14:55:10.315752",
      "status": "completed"
     },
     "tags": []
@@ -1217,28 +1488,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3.vega-embed {\n",
+       "  #altair-viz-be0361d2933444f2a710b494e2407b8b.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3.vega-embed details,\n",
-       "  #altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3.vega-embed details summary {\n",
+       "  #altair-viz-be0361d2933444f2a710b494e2407b8b.vega-embed details,\n",
+       "  #altair-viz-be0361d2933444f2a710b494e2407b8b.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3\"></div>\n",
+       "<div id=\"altair-viz-be0361d2933444f2a710b494e2407b8b\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-f30d287c5900408f9dac8e4c9c1bf0f3\");\n",
+       "    if (outputDiv.id !== \"altair-viz-be0361d2933444f2a710b494e2407b8b\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-be0361d2933444f2a710b494e2407b8b\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -1271,28 +1543,35 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
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\"gridScale\": \"x\", \"domain\": false, \"aria\": false, \"labels\": false, \"ticks\": false, \"zindex\": 0}, {\"scale\": \"x\", \"title\": \"time\", \"orient\": \"bottom\", \"zindex\": 0, \"grid\": false, \"labelOverlap\": true, \"labelFlush\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}}, {\"scale\": \"y\", \"title\": \"value\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"grid\": false, \"zindex\": 0, \"orient\": \"left\"}], \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"circle\", \"title\": \"variable\", \"fill\": \"color\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}], \"background\": \"white\", \"height\": 300, \"padding\": 5, \"style\": \"cell\", \"width\": 600}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.anomalies(test[:72], detected_test[:72])"
+    "(\n",
+    "    on.Plot(test[:72])\n",
+    "    .add(on.marks.line)\n",
+    "    .add(on.marks.mark, data=detected_test[:72].rename({\"generation solar\": \"Anomalies\"}), type=\"dot\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -1300,10 +1579,10 @@
    "id": "2af5c7ba-1617-46f0-9551-67cc51ac1884",
    "metadata": {
     "papermill": {
-     "duration": 0.003676,
-     "end_time": "2024-01-31T17:49:13.217956",
+     "duration": 0.012013,
+     "end_time": "2025-01-06T14:55:10.510372",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.214280",
+     "start_time": "2025-01-06T14:55:10.498359",
      "status": "completed"
     },
     "tags": []
@@ -1314,14 +1593,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "866533ed-bdab-4b29-bc91-50ed0043e84a",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:10.535003Z",
+     "iopub.status.busy": "2025-01-06T14:55:10.534354Z",
+     "iopub.status.idle": "2025-01-06T14:55:10.620752Z",
+     "shell.execute_reply": "2025-01-06T14:55:10.619203Z"
+    },
     "papermill": {
-     "duration": 0.025119,
-     "end_time": "2024-01-31T17:49:13.246629",
+     "duration": 0.101827,
+     "end_time": "2025-01-06T14:55:10.623227",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.221510",
+     "start_time": "2025-01-06T14:55:10.521400",
      "status": "completed"
     },
     "tags": []
@@ -1333,14 +1618,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "id": "44bff6ea-f249-48b6-816f-c49e0d264520",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-01-06T14:55:10.650248Z",
+     "iopub.status.busy": "2025-01-06T14:55:10.649240Z",
+     "iopub.status.idle": "2025-01-06T14:55:10.802101Z",
+     "shell.execute_reply": "2025-01-06T14:55:10.800190Z"
+    },
     "papermill": {
-     "duration": 0.036203,
-     "end_time": "2024-01-31T17:49:13.286631",
+     "duration": 0.167982,
+     "end_time": "2025-01-06T14:55:10.804710",
      "exception": false,
-     "start_time": "2024-01-31T17:49:13.250428",
+     "start_time": "2025-01-06T14:55:10.636728",
      "status": "completed"
     },
     "tags": []
@@ -1351,28 +1642,29 @@
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        "\n",
        "<style>\n",
-       "  #altair-viz-0fbd58bebff649fca3034595965ae986.vega-embed {\n",
+       "  #altair-viz-dd95fa0f304f4baab1da85e25fd2c973.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-0fbd58bebff649fca3034595965ae986.vega-embed details,\n",
-       "  #altair-viz-0fbd58bebff649fca3034595965ae986.vega-embed details summary {\n",
+       "  #altair-viz-dd95fa0f304f4baab1da85e25fd2c973.vega-embed details,\n",
+       "  #altair-viz-dd95fa0f304f4baab1da85e25fd2c973.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-0fbd58bebff649fca3034595965ae986\"></div>\n",
+       "<div id=\"altair-viz-dd95fa0f304f4baab1da85e25fd2c973\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-0fbd58bebff649fca3034595965ae986\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-0fbd58bebff649fca3034595965ae986\");\n",
+       "    if (outputDiv.id !== \"altair-viz-dd95fa0f304f4baab1da85e25fd2c973\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-dd95fa0f304f4baab1da85e25fd2c973\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -1405,51 +1697,41 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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\"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"signal\"}, {\"data\": \"data_0\", \"field\": \"signal\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}], \"axes\": [{\"scale\": \"x\", \"orient\": \"bottom\", \"maxExtent\": 0, \"ticks\": false, \"grid\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"aria\": false, \"labels\": false, \"gridScale\": \"y\", \"zindex\": 0, \"minExtent\": 0, \"domain\": false}, {\"scale\": \"y\", \"grid\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"aria\": false, \"maxExtent\": 0, \"domain\": false, \"minExtent\": 0, \"orient\": \"left\", \"ticks\": false, \"gridScale\": \"x\", \"zindex\": 0, \"labels\": false}, {\"scale\": \"x\", \"title\": \"time\", \"labelFlush\": true, \"orient\": \"bottom\", \"grid\": false, \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0}, {\"scale\": \"y\", \"title\": \"signal\", \"labelOverlap\": true, \"zindex\": 0, \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"grid\": false}], \"height\": 300, \"background\": \"white\", \"padding\": 5, \"style\": \"cell\", \"width\": 500}, {\"mode\": \"vega\"});\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-09-13T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 4599.0}, {\"time\": \"2015-09-13T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 4261.0}, {\"time\": \"2015-09-13T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 3805.0}, {\"time\": \"2015-09-13T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 3118.0}, {\"time\": \"2015-09-13T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 1844.0}, {\"time\": \"2015-09-13T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 747.0}, {\"time\": \"2015-09-13T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 557.0}, {\"time\": \"2015-09-13T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 436.0}, {\"time\": \"2015-09-13T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 327.0}, {\"time\": \"2015-09-13T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 300.0}, {\"time\": \"2015-09-13T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 240.0}, {\"time\": \"2015-09-14T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 138.0}, {\"time\": \"2015-09-14T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 69.0}, {\"time\": \"2015-09-14T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 55.0}, {\"time\": \"2015-09-14T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 48.0}, {\"time\": \"2015-09-14T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 52.0}, {\"time\": \"2015-09-14T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 51.0}, {\"time\": \"2015-09-14T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 170.0}, {\"time\": \"2015-09-14T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 803.0}, {\"time\": \"2015-09-14T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 1816.0}, {\"time\": \"2015-09-14T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 2619.0}, {\"time\": \"2015-09-14T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 3354.0}, {\"time\": \"2015-09-14T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 3743.0}, {\"time\": \"2015-09-14T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 4164.0}, {\"time\": \"2015-09-14T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 4473.0}, {\"time\": \"2015-09-14T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 4514.0}, {\"time\": \"2015-09-14T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 4171.0}, {\"time\": \"2015-09-14T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 3403.0}, {\"time\": \"2015-09-14T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 1872.0}, {\"time\": \"2015-09-14T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 743.0}, {\"time\": \"2015-09-14T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 421.0}, {\"time\": \"2015-09-14T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 296.0}, {\"time\": \"2015-09-14T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 229.0}, {\"time\": \"2015-09-14T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 167.0}, {\"time\": \"2015-09-14T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 128.0}, {\"time\": \"2015-09-15T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 96.0}, {\"time\": \"2015-09-15T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 83.0}, {\"time\": \"2015-09-15T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 66.0}, {\"time\": \"2015-09-15T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 52.0}, {\"time\": \"2015-09-15T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 51.0}, {\"time\": \"2015-09-15T05:00:00.000\", \"variable\": \"generation 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\"2015-09-15T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 1044.0}, {\"time\": \"2015-09-15T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 529.0}, {\"time\": \"2015-09-15T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 281.0}, {\"time\": \"2015-09-15T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 224.0}, {\"time\": \"2015-09-15T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 220.0}, {\"time\": \"2015-09-15T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 116.0}, {\"time\": \"2015-09-15T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 77.0}, {\"time\": \"2015-09-15T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 44.0}, {\"time\": \"2015-09-16T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 41.0}, {\"time\": \"2015-09-16T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 42.0}, {\"time\": \"2015-09-16T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 42.0}, {\"time\": \"2015-09-16T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 37.0}, {\"time\": \"2015-09-16T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 22.0}, {\"time\": \"2015-09-16T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 23.0}, {\"time\": \"2015-09-16T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 171.0}, {\"time\": \"2015-09-16T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 824.0}, {\"time\": \"2015-09-16T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 1512.0}, {\"time\": \"2015-09-16T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1883.0}, {\"time\": \"2015-09-16T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 2155.0}, {\"time\": \"2015-09-16T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 2337.0}, {\"time\": \"2015-09-16T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 2402.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_1\", \"values\": [{\"time\": \"2015-09-13T13:00:00.000\", \"value\": 4599.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-13T14:00:00.000\", \"value\": 4261.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T02:00:00.000\", \"value\": 55.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T03:00:00.000\", \"value\": 48.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T04:00:00.000\", \"value\": 52.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T09:00:00.000\", \"value\": 2619.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T10:00:00.000\", \"value\": 3354.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T11:00:00.000\", \"value\": 3743.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T12:00:00.000\", \"value\": 4164.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-14T13:00:00.000\", \"value\": 4473.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T02:00:00.000\", \"value\": 66.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T03:00:00.000\", \"value\": 52.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T08:00:00.000\", \"value\": 2569.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T09:00:00.000\", \"value\": 3237.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T10:00:00.000\", \"value\": 3401.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T11:00:00.000\", \"value\": 3498.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-15T12:00:00.000\", \"value\": 3149.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T00:00:00.000\", \"value\": 41.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T01:00:00.000\", \"value\": 42.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T07:00:00.000\", \"value\": 824.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T08:00:00.000\", \"value\": 1512.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T09:00:00.000\", \"value\": 1883.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T10:00:00.000\", \"value\": 2155.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-16T11:00:00.000\", \"value\": 2337.0, \"variable\": \"Anomalies\"}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"generation solar\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"Anomalies\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"symbol\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"source_1\"}, \"encode\": {\"update\": {\"shape\": {\"value\": \"circle\"}, \"opacity\": {\"value\": 0.7}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"fill\": {\"field\": \"variable\", \"scale\": \"color\"}}}, \"style\": [\"circle\"]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"maxExtent\": 0, \"grid\": true, \"labels\": false, \"aria\": false, \"minExtent\": 0, \"orient\": \"bottom\", \"ticks\": false, \"zindex\": 0, \"domain\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"gridScale\": \"y\"}, {\"scale\": \"y\", \"aria\": false, \"ticks\": false, \"maxExtent\": 0, \"orient\": \"left\", \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"domain\": false, \"grid\": true, \"labels\": false, \"minExtent\": 0, \"gridScale\": \"x\"}, {\"scale\": \"x\", \"zindex\": 0, \"grid\": false, \"orient\": \"bottom\", \"labelFlush\": true, \"title\": \"time\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}}, {\"scale\": \"y\", \"labelOverlap\": true, \"orient\": \"left\", \"zindex\": 0, \"title\": \"value\", \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}}], \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"circle\", \"title\": \"variable\", \"fill\": \"color\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}], \"padding\": 5, \"width\": 600, \"background\": \"white\", \"height\": 300, \"style\": \"cell\"}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.anomalies(test[:72], predetected[:72])"
+    "(\n",
+    "    on.Plot(test[:72])\n",
+    "    .add(on.marks.line)\n",
+    "    .add(on.marks.mark, data=predetected[:72].rename({\"generation solar\": \"Anomalies\"}), type=\"dot\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d355f16-5c6d-477a-802c-9b1dbf3718f0",
-   "metadata": {
-    "papermill": {
-     "duration": 0.004061,
-     "end_time": "2024-01-31T17:49:13.294603",
-     "exception": false,
-     "start_time": "2024-01-31T17:49:13.290542",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1463,18 +1745,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 7.442652,
-   "end_time": "2024-01-31T17:49:13.919290",
+   "duration": 23.469424,
+   "end_time": "2025-01-06T14:55:13.473916",
    "environment_variables": {},
    "exception": null,
    "input_path": "docs/getting_started.ipynb",
    "output_path": "docs/getting_started.ipynb",
    "parameters": {},
-   "start_time": "2024-01-31T17:49:06.476638",
+   "start_time": "2025-01-06T14:54:50.004492",
    "version": "2.5.0"
   }
  },
diff --git a/docs/run_notebooks.py b/docs/run_notebooks.py
index 8cef6e0..d0596ce 100644
--- a/docs/run_notebooks.py
+++ b/docs/run_notebooks.py
@@ -1,5 +1,14 @@
 import papermill as pm
 from pathlib import Path
+import subprocess
+
+def is_git_ignored(file_path):
+    result = subprocess.run(
+        ["git", "check-ignore", "-q", str(file_path)],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+    )
+    return result.returncode == 0
 
 print("########################################")
 print("#          Notebook Executor           #")
@@ -8,11 +17,14 @@
 current_directory = Path(".")
 files = [file for file in current_directory.rglob("*.ipynb")]
 
+# filter out git-ignored files
+filtered_files = [file for file in files if not is_git_ignored(file)]
+
 print("\n")
 print("Files to execute:\n")
-print(files)
+print(filtered_files)
 
-for file in files:
+for file in filtered_files:
     # print("\n")
     print(f"Executing: {file}")
     pm.execute_notebook(input_path=file, output_path=file, progress_bar=False)
diff --git a/docs/tutorials/0_create-an-anomaly-detection-workflow.ipynb b/docs/tutorials/0_create-an-anomaly-detection-workflow.ipynb
index b4e2a23..75aaecc 100644
--- a/docs/tutorials/0_create-an-anomaly-detection-workflow.ipynb
+++ b/docs/tutorials/0_create-an-anomaly-detection-workflow.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "52af59bb-083c-46c6-989a-bd4c65137a1a",
    "metadata": {
     "papermill": {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "d6fc731f-3f50-4e9a-a24c-b2ab01d4fa31",
    "metadata": {
     "papermill": {
@@ -52,16 +52,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -87,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "ef375aa9-89b6-4ba8-8f06-d874526dda32",
    "metadata": {},
    "outputs": [
@@ -95,101 +86,105 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: prefect in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (2.19.8)\n",
-      "Requirement already satisfied: aiosqlite>=0.17.0 in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (from prefect) (0.20.0)\n",
-      "Requirement already satisfied: alembic<2.0.0,>=1.7.5 in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (from prefect) (1.13.2)\n",
-      "Requirement already satisfied: apprise<2.0.0,>=1.1.0 in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (from prefect) (1.8.0)\n",
-      "Requirement already satisfied: asyncpg>=0.23 in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (from prefect) (0.29.0)\n",
-      "Requirement already satisfied: click<8.2,>=8.0 in /Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages (from prefect) (8.1.7)\n",
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+      "Requirement already satisfied: greenlet!=0.4.17 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from sqlalchemy<3.0.0,>=2.0->sqlalchemy[asyncio]<3.0.0,>=2.0->prefect) (3.1.1)\n",
+      "Requirement already satisfied: shellingham>=1.3.0 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from typer!=0.12.2,<0.14.0,>=0.12.0->prefect) (1.5.4)\n",
+      "Requirement already satisfied: pycparser in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from cffi>=1.12->cryptography>=36.0.1->prefect) (2.22)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from deprecated>=1.2.6->opentelemetry-api<2.0.0,>=1.27.0->prefect) (1.17.0)\n",
+      "Requirement already satisfied: hyperframe<7,>=6.0 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from h2<5,>=3->httpx[http2]!=0.23.2,>=0.23->prefect) (6.0.1)\n",
+      "Requirement already satisfied: hpack<5,>=4.0 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from h2<5,>=3->httpx[http2]!=0.23.2,>=0.23->prefect) (4.0.0)\n",
+      "Requirement already satisfied: zipp>=3.20 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from importlib-metadata<=8.5.0,>=6.0->opentelemetry-api<2.0.0,>=1.27.0->prefect) (3.21.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<14.0,>=11.0->prefect) (0.1.2)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from requests->apprise<2.0.0,>=1.1.0->prefect) (3.4.0)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages (from requests-oauthlib->apprise<2.0.0,>=1.1.0->prefect) (3.2.2)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
      ]
     }
    ],
@@ -217,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "ef3e03e1-c247-4b5a-a27a-a13361e673b0",
    "metadata": {
     "papermill": {
@@ -244,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "5d88b2f2-c34f-45ca-b737-ac4307dcdf5e",
    "metadata": {},
    "outputs": [],
@@ -255,7 +250,8 @@
     "@task\n",
     "def task_detect(ts):\n",
     "    det = on.detectors.quantile(low_quantile=0.1, high_quantile=0.9)\n",
-    "    return det.fit_detect(ts)\n",
+    "    det.fit(ts)\n",
+    "    return det.detect(ts)\n",
     "\n",
     "@task\n",
     "def task_predict(ts):\n",
@@ -281,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "37da3015-b2e7-48db-a601-bc8aafa83c35",
    "metadata": {},
    "outputs": [
@@ -289,20 +285,49 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-P4y4iRhi-py3.10/lib/python3.10/site-packages/prefect/flows.py:1301: UserWarning: `flow.visualize()` will execute code inside of your flow that is not decorated with `@task` or `@flow`.\n",
+      "/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/prefect/flows.py:1374: UserWarning: `flow.visualize()` will execute code inside of your flow that is not decorated with `@task` or `@flow`.\n",
       "  warnings.warn(\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: You don't seem to have any mimeinfo.cache files.\n",
+      "Try running the update-desktop-database command. If you\n",
+      "don't have this command you should install the\n",
+      "desktop-file-utils package. This package is available from\n",
+      "http://freedesktop.org/wiki/Software/desktop-file-utils/\n",
+      "No applications found for mimetype: image/png\n",
+      "./usr/bin/xdg-open: 882: x-www-browser: not found\n",
+      "/usr/bin/xdg-open: 882: firefox: not found\n",
+      "/usr/bin/xdg-open: 882: iceweasel: not found\n",
+      "/usr/bin/xdg-open: 882: seamonkey: not found\n",
+      "/usr/bin/xdg-open: 882: mozilla: not found\n",
+      "/usr/bin/xdg-open: 882: epiphany: not found\n",
+      "/usr/bin/xdg-open: 882: konqueror: not found\n",
+      "/usr/bin/xdg-open: 882: chromium: not found\n",
+      "/usr/bin/xdg-open: 882: chromium-browser: not found\n",
+      "/usr/bin/xdg-open: 882: google-chrome: not found\n",
+      "/usr/bin/xdg-open: 882: www-browser: not found\n",
+      "/usr/bin/xdg-open: 882: links2: not found\n",
+      "/usr/bin/xdg-open: 882: elinks: not found\n",
+      "/usr/bin/xdg-open: 882: links: not found\n",
+      "/usr/bin/xdg-open: 882: lynx: not found\n",
+      "/usr/bin/xdg-open: 882: w3m: not found\n",
+      "xdg-open: no method available for opening 'detection-flow.png'\n"
+     ]
     }
    ],
    "source": [
@@ -320,83 +345,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "746548a8-2c84-4b41-aa84-e1ddc484a95b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.487 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | prefect.engine - Created flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> for flow<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> 'detection-flow'</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:46.487 | \u001b[36mINFO\u001b[0m    | prefect.engine - Created flow run\u001b[35m 'vengeful-corgi'\u001b[0m for flow\u001b[1;35m 'detection-flow'\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.522 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Created task run 'task_detect-0' for task 'task_detect'\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:46.522 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Created task run 'task_detect-0' for task 'task_detect'\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.523 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Executing 'task_detect-0' immediately...\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:46.523 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Executing 'task_detect-0' immediately...\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.566 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_detect-0' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:46.566 | \u001b[36mINFO\u001b[0m    | Task run 'task_detect-0' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.579 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Created task run 'task_predict-0' for task 'task_predict'\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:46.579 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Created task run 'task_predict-0' for task 'task_predict'\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:46.580 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Executing 'task_predict-0' immediately...\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">10:05:21.293 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'tangible-elephant'</span> - Beginning flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'tangible-elephant'</span> for flow<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> 'detection-flow'</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "14:33:46.580 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Executing 'task_predict-0' immediately...\n"
+       "10:05:21.293 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'tangible-elephant'\u001b[0m - Beginning flow run\u001b[35m 'tangible-elephant'\u001b[0m for flow\u001b[1;35m 'detection-flow'\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -405,11 +365,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:47.241 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_predict-0' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">10:05:21.381 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_detect-48e' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "14:33:47.241 | \u001b[36mINFO\u001b[0m    | Task run 'task_predict-0' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
+       "10:05:21.381 | \u001b[36mINFO\u001b[0m    | Task run 'task_detect-48e' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
       ]
      },
      "metadata": {},
@@ -418,11 +378,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:47.252 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Created task run 'task_detect-1' for task 'task_detect'\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">10:05:22.481 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_predict-70b' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "14:33:47.252 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Created task run 'task_detect-1' for task 'task_detect'\n"
+       "10:05:22.481 | \u001b[36mINFO\u001b[0m    | Task run 'task_predict-70b' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
       ]
      },
      "metadata": {},
@@ -431,11 +391,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:47.253 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Executing 'task_detect-1' immediately...\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">10:05:22.556 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_detect-33f' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "14:33:47.253 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Executing 'task_detect-1' immediately...\n"
+       "10:05:22.556 | \u001b[36mINFO\u001b[0m    | Task run 'task_detect-33f' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
       ]
      },
      "metadata": {},
@@ -444,24 +404,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:47.283 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Task run 'task_detect-1' - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">10:05:22.611 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'tangible-elephant'</span> - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "14:33:47.283 | \u001b[36mINFO\u001b[0m    | Task run 'task_detect-1' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">14:33:47.296 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>    | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'vengeful-corgi'</span> - Finished in state <span style=\"color: #008000; text-decoration-color: #008000\">Completed</span>()\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "14:33:47.296 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'vengeful-corgi'\u001b[0m - Finished in state \u001b[32mCompleted\u001b[0m()\n"
+       "10:05:22.611 | \u001b[36mINFO\u001b[0m    | Flow run\u001b[35m 'tangible-elephant'\u001b[0m - Finished in state \u001b[32mCompleted\u001b[0m()\n"
       ]
      },
      "metadata": {},
@@ -482,40 +429,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "11e272b6-b741-4c7b-8d5a-600d7e46dc23",
+   "execution_count": 18,
+   "id": "a86d9a17",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-799070c70ea442b6b9bd8c85e83cef36.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-799070c70ea442b6b9bd8c85e83cef36.vega-embed details,\n",
+       "  #altair-viz-799070c70ea442b6b9bd8c85e83cef36.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-799070c70ea442b6b9bd8c85e83cef36\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-799070c70ea442b6b9bd8c85e83cef36\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-799070c70ea442b6b9bd8c85e83cef36\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"truth\", \"value\": -0.6139672684515424}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"truth\", \"value\": -0.551346108711682}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"truth\", \"value\": -0.16429736674460216}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"truth\", \"value\": -0.22679134338584153}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"truth\", \"value\": 1.5341125547838408}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"truth\", \"value\": 2.045634931069611}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"truth\", \"value\": 1.5317041390166009}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"truth\", \"value\": 1.7557352310874437}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"truth\", \"value\": 2.528069759343351}, 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\"2023-02-05T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.09576088199689892}, {\"time\": \"2023-02-06T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.1059998967975071}, {\"time\": \"2023-02-07T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.10823648044839906}, {\"time\": \"2023-02-08T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.08855796154710749}, {\"time\": \"2023-02-09T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.07434916045163575}, {\"time\": \"2023-02-10T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.07213440404698535}, {\"time\": \"2023-02-11T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.07213440404698535}, {\"time\": \"2023-02-12T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.07213440404698535}, {\"time\": \"2023-02-13T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.06989782039609338}, {\"time\": \"2023-02-14T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.09363426736046865}, {\"time\": \"2023-02-15T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.09418811239949854}, {\"time\": \"2023-02-16T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.12445102071157432}, {\"time\": \"2023-02-17T00:00:00.000\", \"variable\": \"predictions\", \"value\": 0.11005782486059079}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_3\", \"values\": [{\"time\": \"2023-01-01T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-02T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-03T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-04T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-05T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-06T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-07T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-08T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-09T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-10T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-11T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-12T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-13T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-14T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-15T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-16T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-17T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-18T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-19T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-20T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-21T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-22T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-23T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-24T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-25T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-26T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 1.0}, {\"time\": \"2023-01-27T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-28T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-29T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-30T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-01-31T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-01T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-02T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-03T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-04T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-05T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-06T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-07T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-08T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-09T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-10T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-11T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-12T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-13T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-14T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-15T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-16T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}, {\"time\": \"2023-02-17T00:00:00.000\", \"variable\": \"predicted anomalies\", \"value\": 0.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"truth\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"anomalies\"}]}, {\"name\": \"source_2_color_domain_variable_2\", \"values\": [{\"variable\": \"predictions\"}]}, {\"name\": \"source_3_color_domain_variable_3\", \"values\": [{\"variable\": \"predicted anomalies\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_1_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_1\", \"name\": \"faceted_path_layer_1_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"faceted_path_layer_1_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_2_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_2\", \"name\": \"faceted_path_layer_2_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_2_marks\", \"from\": {\"data\": \"faceted_path_layer_2_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_3_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_3\", \"name\": \"faceted_path_layer_3_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_3_marks\", \"from\": {\"data\": \"faceted_path_layer_3_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}, {\"data\": \"source_2\", \"field\": \"time\"}, {\"data\": \"source_3\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}, {\"data\": \"source_2\", \"field\": \"value\"}, {\"data\": \"source_3\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}, {\"data\": \"source_2_color_domain_variable_2\", \"field\": \"variable\"}, {\"data\": \"source_3_color_domain_variable_3\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"gridScale\": \"y\", \"domain\": false, \"orient\": \"bottom\", \"grid\": true, \"aria\": false, \"minExtent\": 0, \"labels\": false, \"ticks\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"maxExtent\": 0, \"zindex\": 0}, {\"scale\": \"y\", \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"grid\": true, \"labels\": false, \"aria\": false, \"maxExtent\": 0, \"ticks\": false, \"minExtent\": 0, \"gridScale\": \"x\", \"zindex\": 0, \"domain\": false}, {\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"labelFlush\": true, \"title\": \"time\", \"zindex\": 0, \"orient\": \"bottom\", \"labelOverlap\": true}, {\"scale\": \"y\", \"title\": \"value\", \"grid\": false, \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"labelOverlap\": true}], \"padding\": 5, \"background\": \"white\", \"height\": 300, \"style\": \"cell\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"width\": 500}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts.plot(label='truth')\n",
-    "ts_ano.plot(label='anomalies')\n",
-    "ts_pred.plot(label='predictions')\n",
-    "ts_pred_ano.plot(label='predicted anomalies');"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, ts.rename({\"random_walk\": \"truth\"}))\n",
+    "    .add(on.marks.line, ts_ano.rename({\"random_walk\": \"anomalies\"}))\n",
+    "    .add(on.marks.line, ts_pred.rename({\"random_walk\": \"predictions\"}))\n",
+    "    .add(on.marks.line, ts_pred_ano.rename({\"random_walk\": \"predicted anomalies\"}))\n",
+    "    .properties(width=500)\n",
+    "    .show()\n",
+    ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b3ebc06-b9d3-4f08-a18b-5b3f03f940a1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -529,7 +543,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.1.1_time-series_data-loading.ipynb b/docs/user_guide/0_core/0.1.1_time-series_data-loading.ipynb
index bed51fe..70c2da3 100644
--- a/docs/user_guide/0_core/0.1.1_time-series_data-loading.ipynb
+++ b/docs/user_guide/0_core/0.1.1_time-series_data-loading.ipynb
@@ -35,16 +35,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -120,13 +111,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -171,7 +163,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -179,7 +171,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -191,6 +185,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -212,7 +210,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -223,7 +221,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -295,15 +293,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -453,35 +451,35 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)&gt; Size: 24B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
        "       [[132.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 1949-03-01\n",
-       "  * component  (component) object &#x27;#Passengers&#x27;\n",
+       "  * Month      (Month) datetime64[ns] 24B 1949-01-01 1949-02-01 1949-03-01\n",
+       "  * component  (component) object 8B &#x27;#Passengers&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2e6388d8-e08a-462b-996c-f7f25470d956' class='xr-array-in' type='checkbox' checked><label for='section-2e6388d8-e08a-462b-996c-f7f25470d956' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9dbef176-d5d2-4ed0-821c-b224dc0c7904' class='xr-array-in' type='checkbox' checked><label for='section-9dbef176-d5d2-4ed0-821c-b224dc0c7904' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
-       "       [[132.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c00550cd-8496-4454-9f59-2a5f34bc6855' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c00550cd-8496-4454-9f59-2a5f34bc6855' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 1949-02-01 1949-03-01</div><input id='attrs-719eb057-0d14-420e-88f5-6e2d660f95fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-719eb057-0d14-420e-88f5-6e2d660f95fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19b35bb8-9058-4982-bc1d-520cd2209030' class='xr-var-data-in' type='checkbox'><label for='data-19b35bb8-9058-4982-bc1d-520cd2209030' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1949-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-91164891-03a0-416a-8400-d1a3cbc34ad0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91164891-03a0-416a-8400-d1a3cbc34ad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b763a19f-c0f2-4925-817f-87869e7a00f9' class='xr-var-data-in' type='checkbox'><label for='data-b763a19f-c0f2-4925-817f-87869e7a00f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdbe298a-fe61-4a58-83ec-e170090ebd3d' class='xr-section-summary-in' type='checkbox'  ><label for='section-cdbe298a-fe61-4a58-83ec-e170090ebd3d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f0e8f7a-d633-4de1-a10b-c3eca714de38' class='xr-index-data-in' type='checkbox'/><label for='index-2f0e8f7a-d633-4de1-a10b-c3eca714de38' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc47fe06-e482-4931-824a-6291520812e7' class='xr-index-data-in' type='checkbox'/><label for='index-dc47fe06-e482-4931-824a-6291520812e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba45fd36-7c44-4563-9a13-d9bd7d0827a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba45fd36-7c44-4563-9a13-d9bd7d0827a6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "       [[132.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-f0bc41ba-e8bf-48ce-a2f0-8e227c00866e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0bc41ba-e8bf-48ce-a2f0-8e227c00866e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 1949-02-01 1949-03-01</div><input id='attrs-170f5fe7-3394-4e6e-b0a1-aacb95e883db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-170f5fe7-3394-4e6e-b0a1-aacb95e883db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fb98649-21cc-47e0-925c-2ef265e650bb' class='xr-var-data-in' type='checkbox'><label for='data-8fb98649-21cc-47e0-925c-2ef265e650bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1949-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-fe91f1fd-0442-4071-8a3f-c73ce66a1f46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe91f1fd-0442-4071-8a3f-c73ce66a1f46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1fef555-a07e-4710-8982-0e6cab98a465' class='xr-var-data-in' type='checkbox'><label for='data-a1fef555-a07e-4710-8982-0e6cab98a465' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a722846-8122-4a65-bedb-615228b653ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a722846-8122-4a65-bedb-615228b653ed' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e5ba47db-c7de-402d-bc22-3ed1810cf51c' class='xr-index-data-in' type='checkbox'/><label for='index-e5ba47db-c7de-402d-bc22-3ed1810cf51c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2c0228c9-e21a-4ac3-ac70-129ee935de9f' class='xr-index-data-in' type='checkbox'/><label for='index-2c0228c9-e21a-4ac3-ac70-129ee935de9f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f52df78-4331-450d-bc3e-7774d5548c8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f52df78-4331-450d-bc3e-7774d5548c8f' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)> Size: 24B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
        "       [[132.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 1949-03-01\n",
-       "  * component  (component) object '#Passengers'\n",
+       "  * Month      (Month) datetime64[ns] 24B 1949-01-01 1949-02-01 1949-03-01\n",
+       "  * component  (component) object 8B '#Passengers'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -555,13 +553,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -606,7 +605,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -614,7 +613,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -626,6 +627,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -647,7 +652,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -658,7 +663,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -730,15 +735,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -888,35 +893,35 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)&gt; Size: 24B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
        "       [[132.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 1949-03-01\n",
-       "  * component  (component) object &#x27;#Passengers&#x27;\n",
+       "  * Month      (Month) datetime64[ns] 24B 1949-01-01 1949-02-01 1949-03-01\n",
+       "  * component  (component) object 8B &#x27;#Passengers&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-501ea229-3f21-4cd8-904c-035fd83ddd57' class='xr-array-in' type='checkbox' checked><label for='section-501ea229-3f21-4cd8-904c-035fd83ddd57' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-67dec334-d8d8-4deb-a77c-bfcca98c5c7e' class='xr-array-in' type='checkbox' checked><label for='section-67dec334-d8d8-4deb-a77c-bfcca98c5c7e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
-       "       [[132.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8658884a-8805-4db6-823c-7a29ca9971a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8658884a-8805-4db6-823c-7a29ca9971a5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 1949-02-01 1949-03-01</div><input id='attrs-46842deb-5aab-465b-846c-4ab84139cae2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46842deb-5aab-465b-846c-4ab84139cae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f58b36d-a201-42dc-aeba-bea1f85ba858' class='xr-var-data-in' type='checkbox'><label for='data-7f58b36d-a201-42dc-aeba-bea1f85ba858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1949-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-f4e78c4b-6366-4273-a986-4bb7b972ddeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4e78c4b-6366-4273-a986-4bb7b972ddeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4c18fc-1c8f-4b40-9243-cfec4bce14a3' class='xr-var-data-in' type='checkbox'><label for='data-de4c18fc-1c8f-4b40-9243-cfec4bce14a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48cf1948-121c-471d-aa1d-e138897aa826' class='xr-section-summary-in' type='checkbox'  ><label for='section-48cf1948-121c-471d-aa1d-e138897aa826' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a18ffe65-a4a9-4bb6-8f11-d289d0e3b74c' class='xr-index-data-in' type='checkbox'/><label for='index-a18ffe65-a4a9-4bb6-8f11-d289d0e3b74c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b7f079a-7b95-4888-9b47-25e240c05de4' class='xr-index-data-in' type='checkbox'/><label for='index-2b7f079a-7b95-4888-9b47-25e240c05de4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a349e675-bbdb-4c94-8461-a7462d3b0540' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a349e675-bbdb-4c94-8461-a7462d3b0540' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "       [[132.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-7610124c-f5c1-42e5-b3e0-8777a8a83fdb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7610124c-f5c1-42e5-b3e0-8777a8a83fdb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 1949-02-01 1949-03-01</div><input id='attrs-334cdd95-c00e-4fc2-b9cd-5cc9d4e86479' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-334cdd95-c00e-4fc2-b9cd-5cc9d4e86479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a78cb0-7577-4836-9c9c-bf32abf28c31' class='xr-var-data-in' type='checkbox'><label for='data-62a78cb0-7577-4836-9c9c-bf32abf28c31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1949-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-587a0f18-9ca5-4f5c-95d1-c0baba9cc429' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-587a0f18-9ca5-4f5c-95d1-c0baba9cc429' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d51a3f81-0d1d-44ec-9b6d-624450b6a58c' class='xr-var-data-in' type='checkbox'><label for='data-d51a3f81-0d1d-44ec-9b6d-624450b6a58c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23d20877-6901-4e92-a425-757daab53aa0' class='xr-section-summary-in' type='checkbox'  ><label for='section-23d20877-6901-4e92-a425-757daab53aa0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c8794c5f-ab4f-4f9f-8352-e0d4b27927f8' class='xr-index-data-in' type='checkbox'/><label for='index-c8794c5f-ab4f-4f9f-8352-e0d4b27927f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7cba19a2-5306-458d-a0b5-cdd98234ac44' class='xr-index-data-in' type='checkbox'/><label for='index-7cba19a2-5306-458d-a0b5-cdd98234ac44' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a436d278-2c32-430c-ba4d-c3a6ecccfeb9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a436d278-2c32-430c-ba4d-c3a6ecccfeb9' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (Month: 3, component: 1, sample: 1)> Size: 24B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
        "       [[132.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 1949-03-01\n",
-       "  * component  (component) object '#Passengers'\n",
+       "  * Month      (Month) datetime64[ns] 24B 1949-01-01 1949-02-01 1949-03-01\n",
+       "  * component  (component) object 8B '#Passengers'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -1118,13 +1123,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1169,7 +1175,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1177,7 +1183,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1189,6 +1197,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1210,7 +1222,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1221,7 +1233,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1293,15 +1305,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1451,7 +1463,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Date: 3, component: 6, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Date: 3, component: 6, sample: 1)&gt; Size: 144B\n",
        "array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
@@ -1473,12 +1485,12 @@
        "        [3.04166698e+00],\n",
        "        [2.95200000e+05]]])\n",
        "Coordinates:\n",
-       "  * Date       (Date) datetime64[ns] 1980-03-17 1980-03-18 1980-03-19\n",
-       "  * component  (component) object &#x27;Open&#x27; &#x27;High&#x27; &#x27;Low&#x27; ... &#x27;Adj_Close&#x27; &#x27;Volume&#x27;\n",
+       "  * Date       (Date) datetime64[ns] 24B 1980-03-17 1980-03-18 1980-03-19\n",
+       "  * component  (component) object 48B &#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Adj_Close&#x27; &#x27;Volume&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Date</span>: 3</li><li><span class='xr-has-index'>component</span>: 6</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8bee3b7c-1722-4b4f-aee0-22a89543162e' class='xr-array-in' type='checkbox' checked><label for='section-8bee3b7c-1722-4b4f-aee0-22a89543162e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 3.302 3.125 3.146 3.146 ... 3.083 3.021 3.042 3.042 2.952e+05</span></div><div class='xr-array-data'><pre>array([[[0.00000000e+00],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Date</span>: 3</li><li><span class='xr-has-index'>component</span>: 6</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-09e1d7b1-bad3-43a9-9292-4b640a6c0fd3' class='xr-array-in' type='checkbox' checked><label for='section-09e1d7b1-bad3-43a9-9292-4b640a6c0fd3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 3.302 3.125 3.146 3.146 ... 3.083 3.021 3.042 3.042 2.952e+05</span></div><div class='xr-array-data'><pre>array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
        "        [3.14583302e+00],\n",
@@ -1497,11 +1509,11 @@
        "        [3.02083302e+00],\n",
        "        [3.04166698e+00],\n",
        "        [3.04166698e+00],\n",
-       "        [2.95200000e+05]]])</pre></div></div></li><li class='xr-section-item'><input id='section-25881044-2058-468b-be5b-9c7370eaf740' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25881044-2058-468b-be5b-9c7370eaf740' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Date</span></div><div class='xr-var-dims'>(Date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-03-17 1980-03-18 1980-03-19</div><input id='attrs-244f57ab-1b06-4a67-a477-d4f603041963' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-244f57ab-1b06-4a67-a477-d4f603041963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81fbbd7b-c0cc-413e-8758-90841ba1e26a' class='xr-var-data-in' type='checkbox'><label for='data-81fbbd7b-c0cc-413e-8758-90841ba1e26a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-03-17T00:00:00.000000000&#x27;, &#x27;1980-03-18T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1980-03-19T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Volume&#x27;</div><input id='attrs-624c1ad7-6677-4340-8be8-9e3c316d4082' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-624c1ad7-6677-4340-8be8-9e3c316d4082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c1146c-8369-4a21-9dd9-9993febf4a7b' class='xr-var-data-in' type='checkbox'><label for='data-a6c1146c-8369-4a21-9dd9-9993febf4a7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24912bca-9ea4-43e8-bb13-07267be357e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-24912bca-9ea4-43e8-bb13-07267be357e8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-df0454f0-0b0c-4105-83a1-863067b7ccd7' class='xr-index-data-in' type='checkbox'/><label for='index-df0454f0-0b0c-4105-83a1-863067b7ccd7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-03-17&#x27;, &#x27;1980-03-18&#x27;, &#x27;1980-03-19&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Date&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81625769-7862-4674-887e-d2cc77deca2a' class='xr-index-data-in' type='checkbox'/><label for='index-81625769-7862-4674-887e-d2cc77deca2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40f62b84-bdf5-416d-8c34-c899b810c6b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40f62b84-bdf5-416d-8c34-c899b810c6b0' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "        [2.95200000e+05]]])</pre></div></div></li><li class='xr-section-item'><input id='section-f2c13328-6e67-47bf-a308-e885aaee899f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2c13328-6e67-47bf-a308-e885aaee899f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Date</span></div><div class='xr-var-dims'>(Date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-03-17 1980-03-18 1980-03-19</div><input id='attrs-29ba6087-d626-40a4-b245-29efb44d9324' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29ba6087-d626-40a4-b245-29efb44d9324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0e0fb8a-26f6-4e70-a68d-94c684385556' class='xr-var-data-in' type='checkbox'><label for='data-b0e0fb8a-26f6-4e70-a68d-94c684385556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-03-17T00:00:00.000000000&#x27;, &#x27;1980-03-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1980-03-19T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Volume&#x27;</div><input id='attrs-138de11c-a085-47f7-a9a1-20bab64a471e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-138de11c-a085-47f7-a9a1-20bab64a471e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c471f9c-d39f-49f8-a1ce-e9dec12c6968' class='xr-var-data-in' type='checkbox'><label for='data-1c471f9c-d39f-49f8-a1ce-e9dec12c6968' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24e89d36-d4b5-48b9-89b9-a14bd642ae9d' class='xr-section-summary-in' type='checkbox'  ><label for='section-24e89d36-d4b5-48b9-89b9-a14bd642ae9d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Date</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3a70f824-ff75-42dc-af35-ee8ff5a95816' class='xr-index-data-in' type='checkbox'/><label for='index-3a70f824-ff75-42dc-af35-ee8ff5a95816' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-03-17&#x27;, &#x27;1980-03-18&#x27;, &#x27;1980-03-19&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Date&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a818b9be-596e-4ef3-b62c-c42aae39dd35' class='xr-index-data-in' type='checkbox'/><label for='index-a818b9be-596e-4ef3-b62c-c42aae39dd35' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-138f2eeb-c240-4954-99da-08d77368e079' class='xr-section-summary-in' type='checkbox'  checked><label for='section-138f2eeb-c240-4954-99da-08d77368e079' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Date: 3, component: 6, sample: 1)>\n",
+       "<TimeSeries (DataArray) (Date: 3, component: 6, sample: 1)> Size: 144B\n",
        "array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
@@ -1523,8 +1535,8 @@
        "        [3.04166698e+00],\n",
        "        [2.95200000e+05]]])\n",
        "Coordinates:\n",
-       "  * Date       (Date) datetime64[ns] 1980-03-17 1980-03-18 1980-03-19\n",
-       "  * component  (component) object 'Open' 'High' 'Low' ... 'Adj_Close' 'Volume'\n",
+       "  * Date       (Date) datetime64[ns] 24B 1980-03-17 1980-03-18 1980-03-19\n",
+       "  * component  (component) object 48B 'Open' 'High' ... 'Adj_Close' 'Volume'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -1610,13 +1622,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1661,7 +1674,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1669,7 +1682,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1681,6 +1696,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1702,7 +1721,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1713,7 +1732,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1785,15 +1804,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1943,35 +1962,35 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 1, sample: 1)&gt;\n",
-       "array([[[0.44888864]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 1, sample: 1)&gt; Size: 24B\n",
+       "array([[[ 0.87897332]],\n",
        "\n",
-       "       [[1.71377365]],\n",
+       "       [[ 1.656084  ]],\n",
        "\n",
-       "       [[2.81188623]]])\n",
+       "       [[-0.3796217 ]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 2022-01-03\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 24B 2022-01-01 2022-01-02 2022-01-03\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d3ccf7d3-4ad3-40b9-a72e-418e1de0d2d9' class='xr-array-in' type='checkbox' checked><label for='section-d3ccf7d3-4ad3-40b9-a72e-418e1de0d2d9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.4489 1.714 2.812</span></div><div class='xr-array-data'><pre>array([[[0.44888864]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-38962585-ada5-4941-88dd-844534e0a116' class='xr-array-in' type='checkbox' checked><label for='section-38962585-ada5-4941-88dd-844534e0a116' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.879 1.656 -0.3796</span></div><div class='xr-array-data'><pre>array([[[ 0.87897332]],\n",
        "\n",
-       "       [[1.71377365]],\n",
+       "       [[ 1.656084  ]],\n",
        "\n",
-       "       [[2.81188623]]])</pre></div></div></li><li class='xr-section-item'><input id='section-156213be-a474-4875-9dfd-c2bd7e918327' class='xr-section-summary-in' type='checkbox'  checked><label for='section-156213be-a474-4875-9dfd-c2bd7e918327' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 2022-01-02 2022-01-03</div><input id='attrs-ad82a93e-a567-40f9-bc84-812d93c27b7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad82a93e-a567-40f9-bc84-812d93c27b7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-740f2acd-dac0-49fa-9f43-99099ae1e27b' class='xr-var-data-in' type='checkbox'><label for='data-740f2acd-dac0-49fa-9f43-99099ae1e27b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-02e1d888-dc57-4acb-b8c7-e32071faa3db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02e1d888-dc57-4acb-b8c7-e32071faa3db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40099ef9-8714-448d-86c2-9839f7b80c09' class='xr-var-data-in' type='checkbox'><label for='data-40099ef9-8714-448d-86c2-9839f7b80c09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-868b5f91-ef94-415e-b5a6-137fa17b9e5b' class='xr-section-summary-in' type='checkbox'  ><label for='section-868b5f91-ef94-415e-b5a6-137fa17b9e5b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3cf8401f-b46b-4a15-b3f1-304b4035cc1c' class='xr-index-data-in' type='checkbox'/><label for='index-3cf8401f-b46b-4a15-b3f1-304b4035cc1c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ca3761b-7d37-445a-9d7c-e676b1c96550' class='xr-index-data-in' type='checkbox'/><label for='index-9ca3761b-7d37-445a-9d7c-e676b1c96550' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e56a0847-3fab-4659-a272-869ea2c9ead5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e56a0847-3fab-4659-a272-869ea2c9ead5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "       [[-0.3796217 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-28642178-ef3c-42d2-9daa-75b09ead4997' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28642178-ef3c-42d2-9daa-75b09ead4997' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 2022-01-02 2022-01-03</div><input id='attrs-dabf65b0-497b-4824-96ed-e71140ae3a92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dabf65b0-497b-4824-96ed-e71140ae3a92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3de6e86-341a-43b7-9df2-6936f0ce0d11' class='xr-var-data-in' type='checkbox'><label for='data-c3de6e86-341a-43b7-9df2-6936f0ce0d11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-17f9ef5c-e244-44a7-8bdb-043734307977' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17f9ef5c-e244-44a7-8bdb-043734307977' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24924cbc-abec-491d-b617-04c50d122357' class='xr-var-data-in' type='checkbox'><label for='data-24924cbc-abec-491d-b617-04c50d122357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9518281-bfd9-4aa7-818a-d0ec37e8c12a' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9518281-bfd9-4aa7-818a-d0ec37e8c12a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1875cf6e-7842-49a3-9b91-dcc6a8280a32' class='xr-index-data-in' type='checkbox'/><label for='index-1875cf6e-7842-49a3-9b91-dcc6a8280a32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f635855d-c1f6-4c21-add5-165382afaa5a' class='xr-index-data-in' type='checkbox'/><label for='index-f635855d-c1f6-4c21-add5-165382afaa5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-299cdb5d-413c-4a7d-8e86-ccf375a6f5f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-299cdb5d-413c-4a7d-8e86-ccf375a6f5f6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 3, component: 1, sample: 1)>\n",
-       "array([[[0.44888864]],\n",
+       "<TimeSeries (DataArray) (time: 3, component: 1, sample: 1)> Size: 24B\n",
+       "array([[[ 0.87897332]],\n",
        "\n",
-       "       [[1.71377365]],\n",
+       "       [[ 1.656084  ]],\n",
        "\n",
-       "       [[2.81188623]]])\n",
+       "       [[-0.3796217 ]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 2022-01-03\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 24B 2022-01-01 2022-01-02 2022-01-03\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -2007,7 +2026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "id": "c9d57d37-6c6a-4d0f-9fc7-f34a95f0cd8f",
    "metadata": {},
    "outputs": [],
@@ -2030,7 +2049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "id": "5798d402-abd5-4eb1-b8ee-fdf3523703fc",
    "metadata": {},
    "outputs": [
@@ -2067,13 +2086,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -2118,7 +2138,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -2126,7 +2146,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -2138,6 +2160,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -2159,7 +2185,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -2170,7 +2196,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -2242,15 +2268,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -2400,62 +2426,62 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 3, sample: 1)&gt;\n",
-       "array([[[0.00966967],\n",
-       "        [0.27866962],\n",
-       "        [0.56073953]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 3, sample: 1)&gt; Size: 72B\n",
+       "array([[[0.41514682],\n",
+       "        [0.92232062],\n",
+       "        [0.64871718]],\n",
        "\n",
-       "       [[0.0105793 ],\n",
-       "        [0.80519664],\n",
-       "        [0.74771133]],\n",
+       "       [[0.06535843],\n",
+       "        [0.6104363 ],\n",
+       "        [0.41561002]],\n",
        "\n",
-       "       [[0.40498576],\n",
-       "        [0.76542589],\n",
-       "        [0.82304112]]])\n",
+       "       [[0.04148376],\n",
+       "        [0.59704985],\n",
+       "        [0.23086672]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2024-02-24T22:00:53.645121 ... 2024-02-2...\n",
-       "  * component  (component) object &#x27;col1&#x27; &#x27;col2&#x27; &#x27;col3&#x27;\n",
+       "  * time       (time) datetime64[ns] 24B 2025-01-06T16:19:02.138076 ... 2025-...\n",
+       "  * component  (component) object 24B &#x27;col1&#x27; &#x27;col2&#x27; &#x27;col3&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 3</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-92fc1721-0b1b-4ab5-a830-0272d4aeb3be' class='xr-array-in' type='checkbox' checked><label for='section-92fc1721-0b1b-4ab5-a830-0272d4aeb3be' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.00967 0.2787 0.5607 0.01058 0.8052 0.7477 0.405 0.7654 0.823</span></div><div class='xr-array-data'><pre>array([[[0.00966967],\n",
-       "        [0.27866962],\n",
-       "        [0.56073953]],\n",
-       "\n",
-       "       [[0.0105793 ],\n",
-       "        [0.80519664],\n",
-       "        [0.74771133]],\n",
-       "\n",
-       "       [[0.40498576],\n",
-       "        [0.76542589],\n",
-       "        [0.82304112]]])</pre></div></div></li><li class='xr-section-item'><input id='section-581cc2f6-5877-4441-85d4-60d69bdd7869' class='xr-section-summary-in' type='checkbox'  checked><label for='section-581cc2f6-5877-4441-85d4-60d69bdd7869' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-02-24T22:00:53.645121 ... 2...</div><input id='attrs-c5e68a7e-67a5-4aaa-9a5b-1a50d98b02d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5e68a7e-67a5-4aaa-9a5b-1a50d98b02d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba65b0bc-6653-41b6-a725-58deb5db1f40' class='xr-var-data-in' type='checkbox'><label for='data-ba65b0bc-6653-41b6-a725-58deb5db1f40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-02-24T22:00:53.645121000&#x27;, &#x27;2024-02-25T22:00:53.645121000&#x27;,\n",
-       "       &#x27;2024-02-26T22:00:53.645121000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;col1&#x27; &#x27;col2&#x27; &#x27;col3&#x27;</div><input id='attrs-441d3c3b-8503-4103-b509-2ad2f6f0c2c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-441d3c3b-8503-4103-b509-2ad2f6f0c2c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5feea734-1971-4024-9b5f-d407fc9c1736' class='xr-var-data-in' type='checkbox'><label for='data-5feea734-1971-4024-9b5f-d407fc9c1736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;col1&#x27;, &#x27;col2&#x27;, &#x27;col3&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5989ac79-bb88-4577-9fe9-635c7da029e1' class='xr-section-summary-in' type='checkbox'  ><label for='section-5989ac79-bb88-4577-9fe9-635c7da029e1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4a6e1377-43fc-4759-8d71-43951e181046' class='xr-index-data-in' type='checkbox'/><label for='index-4a6e1377-43fc-4759-8d71-43951e181046' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-02-24 22:00:53.645121&#x27;, &#x27;2024-02-25 22:00:53.645121&#x27;,\n",
-       "               &#x27;2024-02-26 22:00:53.645121&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0e88077-139d-4d5d-bf55-0dc2e479a285' class='xr-index-data-in' type='checkbox'/><label for='index-d0e88077-139d-4d5d-bf55-0dc2e479a285' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;col1&#x27;, &#x27;col2&#x27;, &#x27;col3&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0900ad1-772f-47d8-b4a6-9b7ad22b69fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0900ad1-772f-47d8-b4a6-9b7ad22b69fa' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 3</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6e480759-bd46-4acc-a0c5-aabc195a4599' class='xr-array-in' type='checkbox' checked><label for='section-6e480759-bd46-4acc-a0c5-aabc195a4599' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.4151 0.9223 0.6487 0.06536 0.6104 0.4156 0.04148 0.597 0.2309</span></div><div class='xr-array-data'><pre>array([[[0.41514682],\n",
+       "        [0.92232062],\n",
+       "        [0.64871718]],\n",
+       "\n",
+       "       [[0.06535843],\n",
+       "        [0.6104363 ],\n",
+       "        [0.41561002]],\n",
+       "\n",
+       "       [[0.04148376],\n",
+       "        [0.59704985],\n",
+       "        [0.23086672]]])</pre></div></div></li><li class='xr-section-item'><input id='section-08a4bb7f-5c67-4ef9-8105-2f53cd066c29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08a4bb7f-5c67-4ef9-8105-2f53cd066c29' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-01-06T16:19:02.138076 ... 2...</div><input id='attrs-c6b6d2f6-5840-4720-be70-9203c381d283' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6b6d2f6-5840-4720-be70-9203c381d283' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbdd6d5a-c925-49df-a6a6-a2d7bd25542d' class='xr-var-data-in' type='checkbox'><label for='data-bbdd6d5a-c925-49df-a6a6-a2d7bd25542d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-01-06T16:19:02.138076000&#x27;, &#x27;2025-01-07T16:19:02.138076000&#x27;,\n",
+       "       &#x27;2025-01-08T16:19:02.138076000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;col1&#x27; &#x27;col2&#x27; &#x27;col3&#x27;</div><input id='attrs-9c005f2b-a943-47b5-901b-b9de7efbba04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c005f2b-a943-47b5-901b-b9de7efbba04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57db1f3e-6d92-4adc-ab69-f799410608a7' class='xr-var-data-in' type='checkbox'><label for='data-57db1f3e-6d92-4adc-ab69-f799410608a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;col1&#x27;, &#x27;col2&#x27;, &#x27;col3&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4eecdd64-997f-4a17-bad4-63fc9eed77a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-4eecdd64-997f-4a17-bad4-63fc9eed77a4' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-636b8b81-0518-498d-9a56-4d13f9a868af' class='xr-index-data-in' type='checkbox'/><label for='index-636b8b81-0518-498d-9a56-4d13f9a868af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2025-01-06 16:19:02.138076&#x27;, &#x27;2025-01-07 16:19:02.138076&#x27;,\n",
+       "               &#x27;2025-01-08 16:19:02.138076&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7b5341e7-c4b7-4390-bf47-6698bc363f4e' class='xr-index-data-in' type='checkbox'/><label for='index-7b5341e7-c4b7-4390-bf47-6698bc363f4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;col1&#x27;, &#x27;col2&#x27;, &#x27;col3&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14180100-f338-4561-8c3c-f27cf6358a9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14180100-f338-4561-8c3c-f27cf6358a9f' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 3, component: 3, sample: 1)>\n",
-       "array([[[0.00966967],\n",
-       "        [0.27866962],\n",
-       "        [0.56073953]],\n",
-       "\n",
-       "       [[0.0105793 ],\n",
-       "        [0.80519664],\n",
-       "        [0.74771133]],\n",
-       "\n",
-       "       [[0.40498576],\n",
-       "        [0.76542589],\n",
-       "        [0.82304112]]])\n",
+       "<TimeSeries (DataArray) (time: 3, component: 3, sample: 1)> Size: 72B\n",
+       "array([[[0.41514682],\n",
+       "        [0.92232062],\n",
+       "        [0.64871718]],\n",
+       "\n",
+       "       [[0.06535843],\n",
+       "        [0.6104363 ],\n",
+       "        [0.41561002]],\n",
+       "\n",
+       "       [[0.04148376],\n",
+       "        [0.59704985],\n",
+       "        [0.23086672]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2024-02-24T22:00:53.645121 ... 2024-02-2...\n",
-       "  * component  (component) object 'col1' 'col2' 'col3'\n",
+       "  * time       (time) datetime64[ns] 24B 2025-01-06T16:19:02.138076 ... 2025-...\n",
+       "  * component  (component) object 24B 'col1' 'col2' 'col3'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2465,19 +2491,11 @@
     "ts = on.TimeSeries.from_data(data, index = index, columns = columns)\n",
     "ts.head(3)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "56e89083-7642-4ef6-ac06-1cbe4e1de7d0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -2491,7 +2509,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.1_time-series.ipynb b/docs/user_guide/0_core/0.1_time-series.ipynb
index 113a630..f4906a6 100644
--- a/docs/user_guide/0_core/0.1_time-series.ipynb
+++ b/docs/user_guide/0_core/0.1_time-series.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "d6fc731f-3f50-4e9a-a24c-b2ab01d4fa31",
    "metadata": {
     "papermill": {
@@ -35,19 +35,9 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import numpy as np\n",
     "import ontime as on"
    ]
   },
@@ -91,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "ef3e03e1-c247-4b5a-a27a-a13361e673b0",
    "metadata": {
     "papermill": {
@@ -118,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "01962643-33af-4adf-8bfa-7d0163e4e41c",
    "metadata": {
     "papermill": {
@@ -164,13 +154,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -215,7 +206,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -223,7 +214,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -235,6 +228,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -256,7 +253,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -267,7 +264,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -339,15 +336,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -497,57 +494,57 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt;\n",
-       "array([[[-0.36981256]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt; Size: 40B\n",
+       "array([[[ 0.49300558]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[ 0.07955401]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[ 0.0028518 ]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[ 0.26707493]],\n",
        "\n",
-       "       [[-3.08642833]]])\n",
+       "       [[-1.48484355]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 40B 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-141b63a2-d126-4a03-8b03-0c269b01338c' class='xr-array-in' type='checkbox' checked><label for='section-141b63a2-d126-4a03-8b03-0c269b01338c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.3698 -1.896 -3.584 -4.196 -3.086</span></div><div class='xr-array-data'><pre>array([[[-0.36981256]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-796b39af-5633-4653-b208-df7281b525c8' class='xr-array-in' type='checkbox' checked><label for='section-796b39af-5633-4653-b208-df7281b525c8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.493 0.07955 0.002852 0.2671 -1.485</span></div><div class='xr-array-data'><pre>array([[[ 0.49300558]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[ 0.07955401]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[ 0.0028518 ]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[ 0.26707493]],\n",
        "\n",
-       "       [[-3.08642833]]])</pre></div></div></li><li class='xr-section-item'><input id='section-22d0f912-a183-42a5-95f1-0c93812dfbb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22d0f912-a183-42a5-95f1-0c93812dfbb1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-01-05</div><input id='attrs-e88e1cc7-dae0-4631-b684-2f1cd5c86409' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e88e1cc7-dae0-4631-b684-2f1cd5c86409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-666bc203-09d7-4717-9cfe-3c3a767c8da9' class='xr-var-data-in' type='checkbox'><label for='data-666bc203-09d7-4717-9cfe-3c3a767c8da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[-1.48484355]]])</pre></div></div></li><li class='xr-section-item'><input id='section-aa545bd2-454b-46a5-b940-8f007396e737' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa545bd2-454b-46a5-b940-8f007396e737' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-01-05</div><input id='attrs-ad3bf067-6806-4f48-a8bd-51f8a172b59f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad3bf067-6806-4f48-a8bd-51f8a172b59f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23f7b51-c523-4e81-9a15-2a5d99aa2358' class='xr-var-data-in' type='checkbox'><label for='data-b23f7b51-c523-4e81-9a15-2a5d99aa2358' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-03T00:00:00.000000000&#x27;, &#x27;2022-01-04T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-0f76d333-e612-4e08-84e0-9703ec531342' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f76d333-e612-4e08-84e0-9703ec531342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2c5438-2055-4c73-b573-d81d4740099f' class='xr-var-data-in' type='checkbox'><label for='data-be2c5438-2055-4c73-b573-d81d4740099f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ac87cb3-5ab7-4e5b-90bb-a276b8fb471e' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ac87cb3-5ab7-4e5b-90bb-a276b8fb471e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89bc0e8b-e62e-400e-8c1a-0a5fe0a23ff7' class='xr-index-data-in' type='checkbox'/><label for='index-89bc0e8b-e62e-400e-8c1a-0a5fe0a23ff7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
+       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-c9fe06a7-c06a-442a-be79-8848d6396a36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9fe06a7-c06a-442a-be79-8848d6396a36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e059c49-87da-4ab1-8e78-c59fbb4191af' class='xr-var-data-in' type='checkbox'><label for='data-4e059c49-87da-4ab1-8e78-c59fbb4191af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a4dcb41-ad9a-4693-85f5-2c9268d053d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a4dcb41-ad9a-4693-85f5-2c9268d053d2' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-414bd92d-c277-46ce-b7d3-5cf9c70bf50d' class='xr-index-data-in' type='checkbox'/><label for='index-414bd92d-c277-46ce-b7d3-5cf9c70bf50d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
        "               &#x27;2022-01-05&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39a83d65-9269-4014-8f6f-a19a24f26edc' class='xr-index-data-in' type='checkbox'/><label for='index-39a83d65-9269-4014-8f6f-a19a24f26edc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a9c8868-1bc8-4ed9-a1f9-c182cca21185' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a9c8868-1bc8-4ed9-a1f9-c182cca21185' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-475f2ff8-ffbc-4e88-967b-5d925055bfb8' class='xr-index-data-in' type='checkbox'/><label for='index-475f2ff8-ffbc-4e88-967b-5d925055bfb8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1a31a8d-63c6-4c84-adb0-a40cb2aa5e16' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1a31a8d-63c6-4c84-adb0-a40cb2aa5e16' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-0.36981256]],\n",
+       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)> Size: 40B\n",
+       "array([[[ 0.49300558]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[ 0.07955401]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[ 0.0028518 ]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[ 0.26707493]],\n",
        "\n",
-       "       [[-3.08642833]]])\n",
+       "       [[-1.48484355]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 40B 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -578,12 +575,12 @@
    "id": "0ec207a0-f270-4f1b-b362-0ad1c0584d2b",
    "metadata": {},
    "source": [
-    "As expected, you can call the `plot()` methdod"
+    "As expected, you can call the `plot()` method"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f",
    "metadata": {
     "papermill": {
@@ -598,31 +595,93 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-083e352fe5b24cc9b154f5eadbfc2127.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-083e352fe5b24cc9b154f5eadbfc2127.vega-embed details,\n",
+       "  #altair-viz-083e352fe5b24cc9b154f5eadbfc2127.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-083e352fe5b24cc9b154f5eadbfc2127\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-083e352fe5b24cc9b154f5eadbfc2127\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-083e352fe5b24cc9b154f5eadbfc2127\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.4930055848866802}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.07955400871245638}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0028518006359509185}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.2670749265477703}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -1.484843547309623}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.1146751496757186}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.8191350556933865}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.025227795796407}, {\"time\": \"2022-01-09T00:00:00.000\", 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\"2022-12-31T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -6.32159648832037}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": -13.575259130613675, \"max\": 4.371978685172342}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"random_walk\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"maxExtent\": 0, \"domain\": false, \"minExtent\": 0, \"labels\": false, \"aria\": false, \"ticks\": false, \"grid\": true, \"zindex\": 0, \"gridScale\": \"y\"}, {\"scale\": \"y\", \"zindex\": 0, \"maxExtent\": 0, \"ticks\": false, \"domain\": false, \"orient\": \"left\", \"minExtent\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"aria\": false, \"grid\": true, \"labels\": false, \"gridScale\": \"x\"}, {\"scale\": \"x\", \"labelFlush\": true, \"labelOverlap\": true, \"grid\": false, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"orient\": \"bottom\", \"title\": \"time\"}, {\"scale\": \"y\", \"orient\": \"left\", \"grid\": false, \"title\": \"value\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0}], \"height\": 200, \"padding\": 5, \"width\": 400, \"background\": \"white\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"style\": \"cell\"}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts.plot();"
+    "ts.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "705ca861-70f0-49a6-a3c9-8da02516012d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -636,7 +695,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.2_detectors.ipynb b/docs/user_guide/0_core/0.2_detectors.ipynb
index 8de7cee..8f13b09 100644
--- a/docs/user_guide/0_core/0.2_detectors.ipynb
+++ b/docs/user_guide/0_core/0.2_detectors.ipynb
@@ -35,16 +35,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -121,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "d463df9c-4f02-4c1e-b1a5-7162b9ea8c63",
    "metadata": {
     "papermill": {
@@ -167,13 +158,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -218,7 +210,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -226,7 +218,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -238,6 +232,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -259,7 +257,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -270,7 +268,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -342,15 +340,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -500,35 +498,35 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 1, sample: 1)&gt;\n",
-       "array([[[-0.00377122]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 3, component: 1, sample: 1)&gt; Size: 24B\n",
+       "array([[[-0.46778339]],\n",
        "\n",
-       "       [[ 0.40349093]],\n",
+       "       [[-1.31388058]],\n",
        "\n",
-       "       [[ 2.47072711]]])\n",
+       "       [[-3.59231678]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 2022-01-03\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 24B 2022-01-01 2022-01-02 2022-01-03\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4a03dfd2-0916-4bdc-a484-e6d0e6065a46' class='xr-array-in' type='checkbox' checked><label for='section-4a03dfd2-0916-4bdc-a484-e6d0e6065a46' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.003771 0.4035 2.471</span></div><div class='xr-array-data'><pre>array([[[-0.00377122]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-23d59e4d-6b26-444e-8e49-1c9b1cd4571d' class='xr-array-in' type='checkbox' checked><label for='section-23d59e4d-6b26-444e-8e49-1c9b1cd4571d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.4678 -1.314 -3.592</span></div><div class='xr-array-data'><pre>array([[[-0.46778339]],\n",
        "\n",
-       "       [[ 0.40349093]],\n",
+       "       [[-1.31388058]],\n",
        "\n",
-       "       [[ 2.47072711]]])</pre></div></div></li><li class='xr-section-item'><input id='section-1a54f908-c6aa-4cd1-a311-bb43ec6de798' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a54f908-c6aa-4cd1-a311-bb43ec6de798' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 2022-01-02 2022-01-03</div><input id='attrs-8876fba7-61fb-4b79-b5c8-e1a2ec8efd8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8876fba7-61fb-4b79-b5c8-e1a2ec8efd8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01070c4e-ebb2-4b26-9280-8c2fc7b02c68' class='xr-var-data-in' type='checkbox'><label for='data-01070c4e-ebb2-4b26-9280-8c2fc7b02c68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-4e26b437-5e5c-40a4-85c7-250ed78963bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e26b437-5e5c-40a4-85c7-250ed78963bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53ecf0e6-33b4-41d3-a9d8-1d026f88fb03' class='xr-var-data-in' type='checkbox'><label for='data-53ecf0e6-33b4-41d3-a9d8-1d026f88fb03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-daaf3f32-8711-4dd6-be16-81180fc90ba6' class='xr-section-summary-in' type='checkbox'  ><label for='section-daaf3f32-8711-4dd6-be16-81180fc90ba6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-460b8ad6-0d25-4e87-9fb9-587d7d2499e8' class='xr-index-data-in' type='checkbox'/><label for='index-460b8ad6-0d25-4e87-9fb9-587d7d2499e8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e086148-640a-4038-b39d-1b1e5e7e2212' class='xr-index-data-in' type='checkbox'/><label for='index-9e086148-640a-4038-b39d-1b1e5e7e2212' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d051c3c3-0913-4ebf-b7d8-23a5fe93e1a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d051c3c3-0913-4ebf-b7d8-23a5fe93e1a8' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "       [[-3.59231678]]])</pre></div></div></li><li class='xr-section-item'><input id='section-595a355c-dc21-4dcf-bc14-c00a4253598d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-595a355c-dc21-4dcf-bc14-c00a4253598d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 2022-01-02 2022-01-03</div><input id='attrs-eec5a2f9-b698-423e-9229-1389c0f789a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eec5a2f9-b698-423e-9229-1389c0f789a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db3cc65-3782-48ab-a217-afbad00e36d9' class='xr-var-data-in' type='checkbox'><label for='data-3db3cc65-3782-48ab-a217-afbad00e36d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-03T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-b6a42073-0648-42cb-b068-ad40fe51787b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6a42073-0648-42cb-b068-ad40fe51787b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0970ae1-3cc2-4e0c-ae9c-022e52cea3a0' class='xr-var-data-in' type='checkbox'><label for='data-d0970ae1-3cc2-4e0c-ae9c-022e52cea3a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d0130b7-d816-45c6-a22a-c79c044fe226' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d0130b7-d816-45c6-a22a-c79c044fe226' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b60a8d7b-10fd-4a25-b59a-a68c316b74b4' class='xr-index-data-in' type='checkbox'/><label for='index-b60a8d7b-10fd-4a25-b59a-a68c316b74b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7da78f1c-719e-41a5-b5a6-15814f797bcb' class='xr-index-data-in' type='checkbox'/><label for='index-7da78f1c-719e-41a5-b5a6-15814f797bcb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97b3c343-f684-4a94-86ea-124ec9a1fdc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97b3c343-f684-4a94-86ea-124ec9a1fdc6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 3, component: 1, sample: 1)>\n",
-       "array([[[-0.00377122]],\n",
+       "<TimeSeries (DataArray) (time: 3, component: 1, sample: 1)> Size: 24B\n",
+       "array([[[-0.46778339]],\n",
        "\n",
-       "       [[ 0.40349093]],\n",
+       "       [[-1.31388058]],\n",
        "\n",
-       "       [[ 2.47072711]]])\n",
+       "       [[-3.59231678]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 2022-01-03\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 24B 2022-01-01 2022-01-02 2022-01-03\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -652,17 +650,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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v29z+5s67MEBaunZDQ0ObX8vFxmj3dTsq5itwzJU55ixwzJWZYM1XZGRgN1eMAuTQoUNyuVzq3bu3b1taWpoOHDjgd1xDQ4OKi4tVUlKixx9/XNHR0crJydHPf/7zZv+2v379eq1Zs8Zv27Rp0zR9+nST4RkpKytr03nV1dUt7quvr1dpaWmr5588ebLJtrq6uibnXfgb4rvvvvMdc+LECd/2qqqqiz5nW7V1jjor5itwzJU55ixwzJUZu+crJSUloOOMAqS2tlZxcXF+2+Li4pq8qX733XdqaGjQnj179Ic//EGnT5/W3Llz1adPH02YMKHJdXNzczVjxgz/gQVxBaSsrEzJyckBV1pjLperxX3R0dHq379/q+dfcsklzV7zwvOio/3/p0lMTPQdc9lllzW73S5W56izYb4Cx1yZY84Cx1yZCfV8GQVIbGys3G633za3293kTblr166SpJ/97Ge65JJLdMkll2jKlCn68MMPmw2QmJiYoMRGayIjI22fcK/X26ZrNjeWC1eKoqKifMc0PjYiIiJov3GCMUcdGfMVOObKHHMWOObKTKjmy+gZ+/Xrp5qaGlVVVfm2lZSU+D7ncV6PHj10+eWXd8if2MmHUAEAsM4oQFwulzIyMpSfn6+6ujoVFhZq//79ysjIaHLs7bffrt/+9rdyu92qrKzUpk2blJ6ebtvAQ4UAAQDAOuM1l/nz5+vIkSPKysrSihUrtGTJEsXHx2vbtm1+HxqdPXu2EhMTNX78eOXm5mrcuHEaP368rYMPBQIEAADrjH8OSEJCglauXNlke3Z2trKzs32Pu3TpokcffVSPPvqotRGGGacCxI7xAAAQrviUjiFWQAAAsI4AMUSAAABgHQFiiAABAMA6AsQQAQIAgHUESBiwEiAAALRHBIghVkAAALCOADFEgAAAYB0BYogAAQDAOgLEEAECAIB1BIihYARIcwgQAEBHRoAYYgUEAADrCBBDBAgAANYRIIYIEAAArCNADBEgAABYR4AYIkAAALCOADFEgAAAYB0BYogAAQDAOgLEkFMBYsd4AAAIVwSIIVZAAACwjgAxRIAAAGAdAWKIAAEAwDoCxBABAgCAdQRIGLASIAAAtEcEiCFWQAAAsI4AMUSAAABgHQFiiAABAMA6AsRQMAKkOQQIAKAjI0AMsQICAIB1BIghAgQAAOsIEEMECAAA1hEghggQAACsI0AMESAAAFhHgBgiQAAAsI4AMUSAAABgHQFiiAABAMA6AsSQUwFix3gAAAhXBIghVkAAALCOADFk9Q2fAAEAgAAxdrE3/LbsJ0AAAJ0NAWKIAAEAwDoCJAxYCRAAANojAsQQKyAAAFhHgBgiQAAAsI4AMRSMAGkOAQIA6MgIEEOsgAAAYB0BYogAAQDAOgLEEAECAIB1BIghAgQAAOsIEEMECAAA1hEghggQAACsI0AMESAAAFhHgBgiQAAAsI4AMUSAAABgHQFiyKkAsWs8AACEIwLEECsgAABYR4AYIkAAALCOADFEgAAAYJ1xgBw/flx5eXlKT0/XlClTtHfv3laPLy8v1y233KInnniizYMMJwQIAADWGQfIsmXL1LNnT+3YsUN5eXlasGCBTp482eLxzzzzjK666ipLg+zorAQIAADtkVGA1NTUqKCgQHPmzFG3bt2UkZGhgQMHateuXc0ev3v3bnm9Xg0fPtyWwYYDVkAAALAu2uTgQ4cOyeVyqXfv3r5taWlpOnDgQJNjz549q+eee05PPfWU/vjHP7Z63fr6etXX1/sPLDpaMTExJsMLiMfj8fuvqYu94Tc0NLR67ebO93q9Fx1P42MaX8Pj8bT5tbTE6hx1NsxX4Jgrc8xZ4JgrM8Gar8jIwNY2jAKktrZWcXFxftvi4uKavQXzyiuv6JZbbtGVV1550euuX79ea9as8ds2bdo0TZ8+3WR4RsrKytp03rlz51rdf+jQIdXU1LS4/8yZM022nThxQqWlpX7bLrxGeXm5oqKiJEkVFRW+7adOnWpyrl3aOkedFfMVOObKHHMWOObKjN3zlZKSEtBxRgESGxsrt9vtt83tdsvlcvltq6qq0ttvv63f/e53AV03NzdXM2bM8B9YEFdAysrKlJycHHClNXaxc6688kq/FaILdenSpcm2yy67TP379/fb1r179ybXPX/M0aNH/Y678FyrrM5RZ8N8BY65MsecBY65MhPq+TIKkH79+qmmpkZVVVXq1auXJKmkpEQTJkzwO+7LL79UZWWlJk+eLOn7v817PB59++23WrVqVZPrxsTEBCU2WhMZGdmmCb/YLZiIiIhWr9vc+c2N5cLHUVFRvm3nV0JaOtYubZ2jzor5ChxzZY45CxxzZSZU82UUIC6XSxkZGcrPz9cvf/lLffTRR9q/f78yMjL8jvvxj3+szZs3+x7/7ne/09GjR/Xwww/bM+oQ4kOoAABYZ5w88+fP15EjR5SVlaUVK1ZoyZIlio+P17Zt23yf2YiJiVFiYqLvKzY2Vl27dtWll15q9/gdR4AAAGCd0QqIJCUkJGjlypVNtmdnZys7O7vZc+bMmWM+sjBFgAAAYB03yQwRIAAAWEeAGCJAAACwjgAxRIAAAGAdAWKIAAEAwDoCxJBTAWLXeAAACEcEiCFWQAAAsI4AMUSAAABgHQFiiAABAMA6AsQQAQIAgHUESBiwEiAAALRHBIihYKyANIcVEABAR0aAGOIWDAAA1hEghggQAACsI0AMESAAAFhHgBgiQAAAsI4AMUSAAABgHQFiiAABAMA6AsQQAQIAgHUEiCECBAAA6wgQQwQIAADWESA2I0AAALg4AsRAIG/2BAgAABdHgBhwMkDsHBMAAOGGADHACggAAPYgQAwQIAAA2IMAMUCAAABgDwLEAAECAIA9CJAwFWiAAADQHhEgBlgBAQDAHgSIAQIEAAB7ECAGQhkgLe0jQAAA7REBYoAAAQDAHgSIAQIEAAB7ECAGCBAAAOxBgBggQAAAsAcBYoAAAQDAHgSIAQIEAAB7ECAGCBAAAOxBgBggQAAAsAcBYoAAAQDAHgSIAScDxM4xAQAQbggQA6yAAABgDwLEAAECAIA9CBADBAgAAPYgQAwEK0CaQ4AAADoyAiQMWFkBAQCgPSJADHALBgAAexAgBggQAADsQYAYsCNAmkOAAAA6GwLEgNUAaWkfAQIA6GwIEAPhEiCmYwIAINwQIAbCKUDO7ydAAADtEQFigAABAMAeBIgBAgQAAHsQIAYIEAAA7EGAGCBAAACwBwFigAABAMAeBIgBAgQAAHsYB8jx48eVl5en9PR0TZkyRXv37m32uBUrVmjSpEkaOXKk7rrrLhUWFloebKg5GSB2jgkAgHBjHCDLli1Tz549tWPHDuXl5WnBggU6efJkk+NcLpdWrlypgoICPfzww1q4cKG++eYbWwYdKqyAAABgj2iTg2tqalRQUKDNmzerW7duysjI0MCBA7Vr1y5NnDjR79g5c+b4fj106FClpqaquLhYV1xxRZPr1tfXq76+3n9g0dGKiYkxGV5APB6P339NNDQ0BHRMS9du6Xyv19vknAvD4sL9jQOkLa+lNVbmqDNivgLHXJljzgLHXJkJ1nxFRga2tmEUIIcOHZLL5VLv3r1929LS0nTgwIFWzzt16pRKSkqUmpra7P7169drzZo1ftumTZum6dOnmwzPSFlZmfE5gazgHDlyRKWlpc3uq66ubnZ7RUVFk3OOHz/u97ila9bX17e4z6q2zFFnxnwFjrkyx5wFjrkyY/d8paSkBHScUYDU1tYqLi7Ob1tcXFyzt2DO83g8evzxx5WZmdnioHJzczVjxgz/gQVxBaSsrEzJyckBV9p5dXV1Fz0mMTFR/fv3b3bf6dOnm93ep0+fJuf07NnT7/GF+8+PvUuXLi0+X1tZmaPOiPkKHHNljjkLHHNlJtTzZRQgsbGxcrvdftvcbrdcLleL5zz55JOqrq7W0qVLWzwmJiYmKLHRmsjISOMJD+TDohERES1et6Xzo6KimpwTFRXl9/jC/Y1vwQTrN05b5qgzY74Cx1yZY84Cx1yZCdV8GT1jv379VFNTo6qqKt+21m6tPPfccyouLtYzzzzjeGAEAx9CBQDAHkYB4nK5lJGRofz8fNXV1amwsFD79+9XRkZGk2PXrl2roqIirVy5ssltm/aKAAEAwB7Gay7z58/XkSNHlJWVpRUrVmjJkiWKj4/Xtm3b/D40unr1ah0+fFg5OTkaMWKERowYoW3bttk6eKcRIAAA2MPoMyCSlJCQoJUrVzbZnp2drezsbN/jffv2WRtZGCJAAACwB5/SMUCAAABgDwLEAAECAIA9CBADBAgAAPYgQAwQIAAA2IMAMUCAAABgDwLEAAECAIA9CBADBAgAAPYgQAwQIAAA2IMAMeBkgNg5JgAAwg0BYoAVEAAA7EGAGCBAAACwBwFiIFgB0hwCBADQkREgYcDKCggAAO0RAWKAWzAAANiDADFAgAAAYA8CxAABAgCAPQgQAwQIAAD2IEAMECAAANiDADFAgAAAYA8CxAABAgCAPQgQAwQIAAD2IEAMECAAANiDADEQTv8aLgECAGjPCBADBAgAAPYgQAwQIAAA2IMAMeBkgFzsuQgQAEB7RoAYCKcVEJMxAQAQbggQA3wXDAAA9iBADAQrQNqCAAEAtGcEiIFwugVDgAAA2jMCJAxYCRAAANojAsQAKyAAANiDADFAgAAAYA8CxAABAgCAPQgQAwQIAAD2IEAMECAAANiDADFAgAAAYA8CxAABAgCAPQgQAwQIAAD2IEAMECAAANiDADEQjgECAEB7RIAYCNcAYRUEANDeECAGCBAAAOxBgBggQAAAsAcBYiCcAiTQ5wQAIBwRIAaCFSBtwQoIAKA9I0AMhNMKCAECAGjPCBADBAgAAPYgQMIAPwcEANDZECAGWAEBAMAeBIgBAgQAAHsQIAYIEAAA7EGAGODbcAEAsAcBYoAAAQDAHgSIAQIEAAB7ECAGCBAAAOxBgBggQAAAsIdxgBw/flx5eXlKT0/XlClTtHfv3maPq6ur08KFCzVy5EhNmDBB7777ruXBhhoBAgCAPaJNT1i2bJl69uypHTt26C9/+YsWLFigjRs3Kj4+3u+4/Px8nThxQlu3btXBgwf1wAMPaPDgwRowYIBdYzdWVFSkoqIiHT9+XAkJCcbf/vrRRx9d9Jh9+/Zp2bJlze4rLi42er7WNB77s88+q9jYWNuu7fV62zxHnRHzFTjmyhxzFjjmyozX61VmZqb69+8fsgEEzO12e4cPH+6tqKjwbZs1a5Z38+bNTY4dM2aM99NPP/U9XrRokXf16tXNXvfMmTPe06dP+33V1tZ6GxoabP1atGiRV1LYfTU31m3btrV6zLhx40I+br744osvvtr315YtW7xnz5619b02UEYrIIcOHZLL5VLv3r1929LS0nTgwAG/406dOqVjx44pLS3N77jPPvus2euuX79ea9as8ds2bdo0TZ8+3WR4F3XixAlbrhMZGalBgwbp66+/liT16dNHtbW1Rte/+uqr9dVXX2nEiBEqLS1tsn/gwIHq27evysvLtXTp0ibHDBkypEPc1gIAhFZZWZmt10tJSQnoOKMAqa2tVVxcnN+2uLg4nTx50m9bTU2Nb1/j42pra5u9bm5urmbMmOE/sOhoxcTEmAzvombPnq0RI0bo6NGjSkxMbPMS3Y033qg+ffqooKBAZ86c0ahRo3T27FkVFhbK4/Fc9Pyrr75agwYN0scff6whQ4a0+Dr/9re/6eDBgxoyZEiTfb/+9a81ceJEVVRUtOk1tMbr9Vqeo86E+Qocc2WOOQscc2XG6/UqOTlZycnJiox0/ntSjAIkNjZWbrfbb5vb7ZbL5fLbdv6x2+1W9+7dfb9u6XMKMTExtsdGc6655hoNHjxYpaWl6t+/v+UJHzt2rN/jO+64w+j84cOHt7r/0ksv1Q033NDi/h//+MdGzxcoj8dj2xx1BsxX4Jgrc8xZ4JgrM+fnKzIyMiTzZfSM/fr1U01NjaqqqnzbSkpKlJqa6ndcjx491LNnT+3fv9/vuIEDB1ocLgAA6AiMAsTlcikjI0P5+fmqq6tTYWGh9u/fr4yMjCbHjh8/XuvWrZPb7dYXX3yhXbt2NVkxAAAAnZPxmsv8+fN15MgRZWVlacWKFVqyZIni4+O1bds2vw+NzpkzRz169NC4ceP0r//6r/rVr34V0m/BBQAA4cP454AkJCRo5cqVTbZnZ2crOzvb97hbt276t3/7N2ujAwAAHRKf0gEAAI4jQAAAgOMIEAAA4DgCBAAAOI4AAQAAjiNAAACA4wgQAADgOAIEAAA4jgABAACOI0AAAIDjIrxerzfUgwAAAJ0LKyAAAMBxBAgAAHAcAQIAABxHgAAAAMcRIAAAwHEECAAAcBwBAgAAHEeAAAAAxxEgAADAcQQIgJApLy/X8OHDQz0MACHQrgOkvr5ejz/+uCZMmKCMjAzNnDlTn332mW//hg0bdOuttyozM1PPPfeczv/U+b///e968MEHdeuttyorK0u//OUvdeTIEd95K1as0KRJkzRy5EjdddddKiwsdPy1BUtOTo7S09NVW1vr21ZXV6eRI0cqJycnhCMLP8yVNTk5OfrrX/8a6mGEvU8++UQzZ85URkaGsrKydO+99+qbb74J9bDCTk5Ojm6//XadO3fOt23JkiXKz88P4ajCR7DeD/Pz833XnDx5sjZv3mzbmNt1gDQ0NKhv37566aWXtHPnTv3jP/6jHnzwQdXU1KioqEivv/66NmzYoNdee01//vOffRNXXV2t0aNHa+PGjdq2bZt69eqlxYsX+67rcrm0cuVKFRQU6OGHH9bChQs71P8h9OrVSwUFBb7HBQUFSkxMNL5O4/8j6KjsmiugOdXV1Zo3b55+9rOfaefOndqyZYvuvPNORUVFhXpoYammpkZvv/12qIcRloL1fpidna033nhDu3bt0rPPPqtVq1Zp//79toy5XQdIbGysZs2apaSkJEVGRmrs2LHq0qWLSktLtXXrVk2ePFlXXnmlEhMTdffdd2vr1q2SpOuuu04TJ05Ujx49FBMTo+nTp+vzzz/3XXfOnDnq37+/IiMjNXToUKWmpqq4uDhUL9N2Y8eO1bZt23yPt23bpnHjxvker1u3TrfffrsyMjKUm5ur//3f//Xty8nJ0X/+539q6tSpmjx5sqPjDoW2ztW7776r2bNn+11r0aJFWrdunTMDDyOLFy/W2rVrfY+3bNmi++67L4QjCh+lpaWKiYlRZmamIiMj5XK5NHr0aCUlJamhoUH5+fm6/fbbNWbMGK1YscIX/fn5+XrkkUc0b948jRw5UrNmzVJ5eXmIX03w/fSnP9X69eub/cvP66+/rkmTJunWW2/VwoULVV1dLUm677779M477/iOO7+KWVFR4di4nRCs98N+/fopNjZWkhQRESFJtv2FvF0HyIUOHTqkU6dOKTk5WQcPHtSgQYN8+9LS0lRSUtLseZ9++qlSU1Ob3Xfq1CmVlJS0uL89Gjp0qEpKSnT8+HEdP35c+/fv17Bhw3z7BwwYoJdfflkffPCBhg8frkWLFvmdv3PnTuXn5+v11193euiOa+tcjRo1Sl9//bWqqqokSWfOnFFBQYHGjh0bkteB8NS/f3+dPXtWTzzxhPbs2eN705SkV155RZ9++qlefvllvfnmmyouLtabb77p2/+nP/1Jd9xxhz744ANdd911Tf6cdkRDhw5VUlKStmzZ4rd9z549Wrt2rVasWKEtW7aorq5OTz31lCTptttu0/bt233HFhYWKi0tTUlJSY6O3Wl2vh9u2LBB6enpmjJlinr16mXb57Y6TIDU1dVp4cKFmjlzprp3766amhrFxcX59sfFxfndyz+vrKxML7zwgu6///4m+zwejx5//HFlZmYqJSUlqON3UlRUlDIzM/X+++/r/fffV2Zmpt+Sb2ZmphISEhQdHe37W31NTY1v/1133aWePXuqW7duoRi+o9o6V926dVNGRobef/99SVJRUZEGDhyoK664IlQvBWGoe/fu+s1vfqP6+notWrRIt912mxYuXCi3263NmzfrF7/4hRISEnTJJZfo7rvv1gcffOA79wc/+IFGjBihLl26aM6cOfr888/97t13VLNmzWqyCvL+++9rypQpSk1NVWxsrO6//35t375dXq9Xo0eP1ieffKJTp05JkrZv364xY8aEaviOsPv9cObMmSosLNSGDRuUmZmp6OhoW8Zpz1VC7Ny5c5o/f76Sk5M1a9YsSd9/jsPtdvuOcbvdvmWk844cOaK5c+fq3nvv1Q9/+MMm133yySdVXV2tpUuXBvcFhEB2draeeuopeb1ePfzww/J4PL59mzZt0quvvqrKykpFRETI6/Xq5MmTcrlckqTevXuHatgh0da5Gj9+vFatWqW7775b7733HqsfaFZaWpqeeOIJSdJXX32l+fPna926daqoqNADDzzgW/b2er3q1auX77zGfw67deum+Ph4HT16VJdffrmzL8Bhw4YN0+WXX+53W+Xo0aP6wQ9+4Hvcp08fnTlzRidPntSll16qG264QTt37tRtt92m3bt361e/+lUohu6IYL0fRkRE6LrrrtPWrVu1adMmTZs2zfJY232AeDweLVy4UBEREVq8eLHvD2tKSor279+vjIwMSVJJSYkGDhzoO+/EiRO67777NHnyZP3kJz9pct3nnntOxcXFevHFFxUTE+PMi3HQNddco5MnT0qSrr32Wt89v/Lycj399NP6zW9+o8GDB6u+vl4jRozwfWJa+r/7gJ1FW+fqhz/8oaqqqlRcXKw9e/ZowYIFIXsNoRQbG6szZ874Hh87diyEowlvV199tUaPHq2SkhL16tVLS5cu1eDBg5s9trKy0vfruro6nTx5stN8QHrWrFlaunSpbrrpJklSYmKi33xUVFSoa9euio+PlySNGTNG7733nrp27aqrr766w85TsN4PG2toaFBZWZkt4233t2CWLFmiY8eO6cknn/RbFho/frw2btyow4cP69ixY3rllVc0fvx4Sd9/6nfu3LlKT0/XzJkzm1xz7dq1Kioq0sqVK/2WrTqa5cuXa/ny5X7bampqFBkZqYSEBN+H4NC2uYqKitKYMWP02GOP6frrr1dCQoKTQw4bgwYN0ocffqjq6modPnyY72Jo5O9//7teeeUV362T0tJS/fd//7euvfZaTZw4UatWrdLRo0fl9XpVXl6ujz/+2HfuZ599pqKiIp09e1Zr1qzRtdde2+FXP867+eab1bNnT+3atUvS94GxadMmHTx4ULW1tVq1apVuvfVW3xvwqFGj9Ne//lVvvvlmh779Eoz3w02bNun06dPyeDzat2+f3n333WZXSNqiXa+AfPvtt3rrrbfUtWtX3Xrrrb7tK1euVHp6uqZOnap77rlHHo9Hd9xxhyZNmiTp+2+lLC4uVmlpqd544w3feed/3sfq1avVpUsXv5/18Mgjjyg7O9uhV+aM5j5Ym5aWpilTpuiuu+5SbGys/vmf/1ldunQJwejCS1vnavz48fr973+ve+65x6mhhp3x48dr9+7dmjBhggYMGKCxY8fqf/7nf0I9rLDgcrn02Wef6be//a3cbrfi4+OVlZWlmTNnKiIiQg0NDfr5z3+uEydOKCkpye/3UWZmpjZt2qQFCxboH/7hH/T//t//C+Ercd6sWbP0L//yL5K+D5KZM2cqLy9PbrdbN998s+bNm+c7tnv37ho2bJiKioq0bNmyUA05qIL1flhYWKjnn39eZ8+eVVJSkvLy8jRixAhbxhzhbby2DsBWx48f18SJE/Xee+/5PkPTWWRlZemll17SgAEDQj2UDic/P19VVVVauHBhqIcCtFm7vwUDhCuv16s//OEPysrK6nTxsW/fPnm9XvXp0yfUQwEQptr1LRggnI0bN05xcXF6/vnnQz0UR/3617/Wnj179Mgjj6hr166hHg6AMMUtGAAA4DhuwQAAAMcRIAAAwHEECAAAcBwBAgAAHEeAAAAAxxEgAGyxb98+DR06VEOHDlV5eXmohwMgzBEgAIwtXrxYQ4cO1ezZs33bunfvruuuu07XXXddh/wHHAHYix9EBsAWgwcP1oYNG0I9DADtBD+IDICRnJwcffvtt022r169Wvfee68k6e2331bfvn21ePFivfPOO+rTp4/mzJmjF198UdXV1Zo4caLuv/9+vfDCC3r77bfVvXt35ebmaurUqb7rHTlyRKtWrdLu3bt14sQJ9e7dWzk5OZo5c6bfv/QJoH3iTzEAI1dddZVqa2t14sQJxcXFKSUlRZJUXFzc4jlHjx7Vk08+qcTERLndbr366qvas2ePqqqq1L17d1VWVurf//3fddNNNyklJUUnTpzQzJkzVVlZ6XuOAwcOaPXq1frmm2+0aNEip14ugCDhMyAAjDz11FNKT0+X9H2MbNiwQRs2bNDgwYNbPOfs2bN6/vnntXHjRvXu3VuSVFZWpldffVWvv/66unbtKo/Ho48//liS9Nprr6myslI9e/bUW2+9pVdffdX3z6i/8847KisrC/KrBBBsrIAACLoePXro+uuvlyQlJSWpsrJSAwcOVN++fSVJCQkJqqio0HfffSdJ+tvf/iZJOnbsmG677Ta/a3m9Xn3xxRdKTk527gUAsB0BAiDo4uLifL+Oiopqsi0iIkLS93Fx4Xnnb/E01q1bt2AME4CDCBAAxs4HQF1dXVCuf8011+jDDz9UVFSUlixZ4lspcbvd2rlzp0aPHh2U5wXgHAIEgLEBAwZIkr788kvdeeedio2N1axZs2y7/vTp07V582ZVVVXpJz/5iVJSUuR2u1VZWalz587p9ttvt+25AIQGH0IFYGzixInKzMxU9+7dVVJSoi+++EIej8e26yckJGj9+vXKyclRfHy8SkpKdObMGd1www166KGHbHseAKHDzwEBAACOYwUEAAA4jgABAACOI0AAAIDjCBAAAOA4AgQAADiOAAEAAI4jQAAAgOMIEAAA4DgCBAAAOI4AAQAAjiNAAACA4/4/92lW1LPlkmwAAAAASUVORK5CYII=",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-d5be512343994241b061a534bc888b2d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-d5be512343994241b061a534bc888b2d.vega-embed details,\n",
+       "  #altair-viz-d5be512343994241b061a534bc888b2d.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-d5be512343994241b061a534bc888b2d\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-d5be512343994241b061a534bc888b2d\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-d5be512343994241b061a534bc888b2d\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-10T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-11T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-12T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-13T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-14T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-15T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-16T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-17T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-18T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-19T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-20T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": \"2022-01-21T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.0}, {\"time\": 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+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 9,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ats.plot();"
+    "ats.plot()"
    ]
   },
   {
@@ -800,17 +868,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-6a2dff0f671c47a7b3408d1f993c754f.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-6a2dff0f671c47a7b3408d1f993c754f.vega-embed details,\n",
+       "  #altair-viz-6a2dff0f671c47a7b3408d1f993c754f.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-6a2dff0f671c47a7b3408d1f993c754f\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-6a2dff0f671c47a7b3408d1f993c754f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-6a2dff0f671c47a7b3408d1f993c754f\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-10T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-11T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-12T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-13T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-14T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-15T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-16T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-17T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-18T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-19T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-20T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-21T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": 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\"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"domain\": false, \"maxExtent\": 0, \"orient\": \"bottom\", \"grid\": true, \"gridScale\": \"y\", \"labels\": false, \"aria\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"minExtent\": 0, \"zindex\": 0, \"ticks\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"ticks\": false, \"grid\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"domain\": false, \"maxExtent\": 0, \"labels\": false, \"minExtent\": 0, \"gridScale\": \"x\", \"zindex\": 0, \"aria\": false}, {\"scale\": \"x\", \"title\": \"time\", \"labelFlush\": true, \"labelOverlap\": true, \"zindex\": 0, \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"labelOverlap\": true, \"zindex\": 0, \"title\": \"value\"}], \"background\": \"white\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"padding\": 5, \"width\": 400, \"height\": 200, \"style\": \"cell\"}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ats.plot();"
+    "ats.plot()"
    ]
   },
   {
@@ -831,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 15,
    "id": "349e0ede-794b-42c9-af57-e8c9d809aad5",
    "metadata": {},
    "outputs": [],
@@ -850,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 16,
    "id": "e00da952-230e-40a1-a547-20b8c31b0d7f",
    "metadata": {},
    "outputs": [
@@ -858,7 +996,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['time', '0'], dtype='object', name='component')\n"
+      "Index(['time', 'random_walk'], dtype='object', name='component')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/benjy/projects_dev/ontime/src/ontime/core/utils/anomaly_logger.py:46: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  self.log = pd.concat([self.log, log_df])\n"
      ]
     }
    ],
@@ -876,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 17,
    "id": "6b13aa0c-4987-49b5-a2f0-da050c2dab7d",
    "metadata": {},
    "outputs": [
@@ -911,7 +1057,7 @@
        "      <th>0</th>\n",
        "      <td>2022-01-01</td>\n",
        "      <td>QuantileDetector</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -980,7 +1126,7 @@
       ],
       "text/plain": [
        "     Timestamp       Description  Value\n",
-       "0   2022-01-01  QuantileDetector   True\n",
+       "0   2022-01-01  QuantileDetector  False\n",
        "1   2022-01-02  QuantileDetector  False\n",
        "2   2022-01-03  QuantileDetector  False\n",
        "3   2022-01-04  QuantileDetector  False\n",
@@ -995,7 +1141,7 @@
        "[365 rows x 3 columns]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1007,7 +1153,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1021,7 +1167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.3_generators.ipynb b/docs/user_guide/0_core/0.3_generators.ipynb
index 01729fd..390c17a 100644
--- a/docs/user_guide/0_core/0.3_generators.ipynb
+++ b/docs/user_guide/0_core/0.3_generators.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe",
    "metadata": {
     "papermill": {
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f",
    "metadata": {
     "papermill": {
@@ -35,16 +35,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -104,17 +95,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-15ba842a0ac4422a8f08c289ff828f7d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-15ba842a0ac4422a8f08c289ff828f7d.vega-embed details,\n",
+       "  #altair-viz-15ba842a0ac4422a8f08c289ff828f7d.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-15ba842a0ac4422a8f08c289ff828f7d\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-15ba842a0ac4422a8f08c289ff828f7d\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-15ba842a0ac4422a8f08c289ff828f7d\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot();"
+    "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot()"
    ]
   },
   {
@@ -136,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "ae82840b-2bf5-4d9b-936c-7355cd7da95d",
    "metadata": {
     "papermill": {
@@ -151,17 +212,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-5a12c37d921e4bc2af0ef09e2125b422.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-5a12c37d921e4bc2af0ef09e2125b422.vega-embed details,\n",
+       "  #altair-viz-5a12c37d921e4bc2af0ef09e2125b422.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-5a12c37d921e4bc2af0ef09e2125b422\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-5a12c37d921e4bc2af0ef09e2125b422\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-5a12c37d921e4bc2af0ef09e2125b422\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"gaussian\", \"value\": -0.5714634308189396}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"gaussian\", \"value\": 0.642915457084146}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"gaussian\", \"value\": -0.5634330515626459}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"gaussian\", \"value\": -0.45120855435449825}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"gaussian\", \"value\": 0.5943888147691346}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"gaussian\", \"value\": -0.17757617521775287}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"gaussian\", \"value\": 0.060238811065888166}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"gaussian\", \"value\": 1.658217046104463}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"gaussian\", 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\"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"aria\": false, \"zindex\": 0, \"ticks\": false, \"maxExtent\": 0, \"gridScale\": \"y\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": true, \"orient\": \"bottom\", \"minExtent\": 0, \"domain\": false, \"labels\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"ticks\": false, \"labels\": false, \"maxExtent\": 0, \"grid\": true, \"domain\": false, \"aria\": false, \"gridScale\": \"x\", \"minExtent\": 0, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}}, {\"scale\": \"x\", \"title\": \"time\", \"labelOverlap\": true, \"zindex\": 0, \"grid\": false, \"orient\": \"bottom\", \"labelFlush\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}}, {\"scale\": \"y\", \"labelOverlap\": true, \"title\": \"value\", \"grid\": false, \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0}], \"padding\": 5, \"background\": \"white\", \"style\": \"cell\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"width\": 400, \"height\": 200}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();"
+    "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot()"
    ]
   },
   {
@@ -183,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "9802955b-6791-43bb-80d4-74ccb4119d70",
    "metadata": {
     "papermill": {
@@ -198,40 +329,93 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-0f001dd4363146ddabb8661297d20133.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-0f001dd4363146ddabb8661297d20133.vega-embed details,\n",
+       "  #altair-viz-0f001dd4363146ddabb8661297d20133.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-0f001dd4363146ddabb8661297d20133\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-0f001dd4363146ddabb8661297d20133\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-0f001dd4363146ddabb8661297d20133\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.8703867663424528}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -1.6621499220364886}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.6955976243117028}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.7297889074347695}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.18857106303698767}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.3311740613284813}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.8515679003693923}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.6972030683583914}, {\"time\": \"2022-01-09T00:00:00.000\", 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\"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"gridScale\": \"y\", \"ticks\": false, \"zindex\": 0, \"maxExtent\": 0, \"grid\": true, \"aria\": false, \"labels\": false, \"minExtent\": 0, \"domain\": false}, {\"scale\": \"y\", \"ticks\": false, \"zindex\": 0, \"domain\": false, \"grid\": true, \"minExtent\": 0, \"orient\": \"left\", \"gridScale\": \"x\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"aria\": false, \"maxExtent\": 0, \"labels\": false}, {\"scale\": \"x\", \"orient\": \"bottom\", \"labelFlush\": true, \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"title\": \"time\", \"grid\": false}, {\"scale\": \"y\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"orient\": \"left\", \"grid\": false, \"title\": \"value\", \"labelOverlap\": true}], \"width\": 400, \"padding\": 5, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"style\": \"cell\", \"background\": \"white\", \"height\": 200}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();"
+    "on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb55754b-215a-457e-9577-2571c6d81c6d",
-   "metadata": {
-    "papermill": {
-     "duration": 0.002604,
-     "end_time": "2024-01-31T17:50:30.279671",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:30.277067",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -245,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.4_models.ipynb b/docs/user_guide/0_core/0.4_models.ipynb
index 296351b..a43d0a1 100644
--- a/docs/user_guide/0_core/0.4_models.ipynb
+++ b/docs/user_guide/0_core/0.4_models.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe",
    "metadata": {
     "ExecuteTime": {
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f",
    "metadata": {
     "ExecuteTime": {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "id": "e9a96d79-0423-4d79-b01d-726193216238",
    "metadata": {
     "ExecuteTime": {
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "id": "a1b679c1-4334-4d10-9ef1-019e81a36b90",
    "metadata": {
     "ExecuteTime": {
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "id": "85663124-29d5-4da0-8cee-e10639eb8c32",
    "metadata": {},
    "outputs": [],
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "id": "a83733e8-fb76-4d6d-94c0-bc5b255d2951",
    "metadata": {},
    "outputs": [
@@ -222,6 +222,8 @@
       "lightning.pytorch.utilities.rank_zero INFO  TPU available: False, using: 0 TPU cores\n",
       "INFO: HPU available: False, using: 0 HPUs\n",
       "lightning.pytorch.utilities.rank_zero INFO  HPU available: False, using: 0 HPUs\n",
+      "INFO: You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "lightning.pytorch.utilities.rank_zero INFO  You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
       "\n",
       "  | Name            | Type             | Params | Mode \n",
@@ -242,17 +244,10 @@
       "0         Modules in eval mode\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bc464824aed4fd0b23af7f8c4dc9828",
+       "model_id": "7a7542700a2c41a0a67c572f9190f383",
        "version_major": 2,
        "version_minor": 0
       },
@@ -274,10 +269,10 @@
     {
      "data": {
       "text/plain": [
-       "<ontime.core.modelling.model.Model at 0x7f555264c520>"
+       "<ontime.core.modelling.model.Model at 0x7f7accf17460>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "id": "9f7f91dc-5cb0-4ee4-885e-2d7ea16e4762",
    "metadata": {},
    "outputs": [
@@ -309,7 +304,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40832e277e0648e0b605bee62324f662",
+       "model_id": "0cafc6349c39461d84421fae4dd19c4e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -336,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 8,
    "id": "0b0ee849",
    "metadata": {},
    "outputs": [
@@ -356,7 +351,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "22f6ceb87de042cca5e5cf1f0eff9e77",
+       "model_id": "d4e5df701233492dbcca9436aa693ab3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -383,7 +378,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "314368eea4fa406f95f7c76916596644",
+       "model_id": "adb957881ede4b41863436e32f7de6e4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -398,25 +393,25 @@
      "data": {
       "text/plain": [
        "[<TimeSeries (DataArray) (time: 10, component: 1, sample: 1)> Size: 40B\n",
-       " array([[[-2.114943 ]],\n",
+       " array([[[14.379956]],\n",
        " \n",
-       "        [[-1.5462902]],\n",
+       "        [[13.368615]],\n",
        " \n",
-       "        [[-2.2913358]],\n",
+       "        [[13.446395]],\n",
        " \n",
-       "        [[-2.0579665]],\n",
+       "        [[15.118863]],\n",
        " \n",
-       "        [[-2.686432 ]],\n",
+       "        [[14.379672]],\n",
        " \n",
-       "        [[-2.4956915]],\n",
+       "        [[13.368358]],\n",
        " \n",
-       "        [[-2.6606524]],\n",
+       "        [[13.446113]],\n",
        " \n",
-       "        [[-2.825928 ]],\n",
+       "        [[15.118556]],\n",
        " \n",
-       "        [[-2.5841327]],\n",
+       "        [[13.832502]],\n",
        " \n",
-       "        [[-2.8603108]]], dtype=float32)\n",
+       "        [[14.525592]]], dtype=float32)\n",
        " Coordinates:\n",
        "   * time       (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10\n",
        "   * component  (component) object 8B 'random_walk'\n",
@@ -425,25 +420,25 @@
        "     static_covariates:  None\n",
        "     hierarchy:          None,\n",
        " <TimeSeries (DataArray) (time: 10, component: 1, sample: 1)> Size: 40B\n",
-       " array([[[-2.114943 ]],\n",
+       " array([[[14.379956]],\n",
        " \n",
-       "        [[-1.5462902]],\n",
+       "        [[13.368615]],\n",
        " \n",
-       "        [[-2.2913358]],\n",
+       "        [[13.446395]],\n",
        " \n",
-       "        [[-2.0579665]],\n",
+       "        [[15.118863]],\n",
        " \n",
-       "        [[-2.686432 ]],\n",
+       "        [[14.379672]],\n",
        " \n",
-       "        [[-2.4956915]],\n",
+       "        [[13.368358]],\n",
        " \n",
-       "        [[-2.6606524]],\n",
+       "        [[13.446113]],\n",
        " \n",
-       "        [[-2.825928 ]],\n",
+       "        [[15.118556]],\n",
        " \n",
-       "        [[-2.5841327]],\n",
+       "        [[13.832502]],\n",
        " \n",
-       "        [[-2.8603108]]], dtype=float32)\n",
+       "        [[14.525592]]], dtype=float32)\n",
        " Coordinates:\n",
        "   * time       (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10\n",
        "   * component  (component) object 8B 'random_walk'\n",
@@ -452,25 +447,25 @@
        "     static_covariates:  None\n",
        "     hierarchy:          None,\n",
        " <TimeSeries (DataArray) (time: 10, component: 1, sample: 1)> Size: 40B\n",
-       " array([[[-2.114943 ]],\n",
+       " array([[[14.379956]],\n",
        " \n",
-       "        [[-1.5462902]],\n",
+       "        [[13.368615]],\n",
        " \n",
-       "        [[-2.2913358]],\n",
+       "        [[13.446395]],\n",
        " \n",
-       "        [[-2.0579665]],\n",
+       "        [[15.118863]],\n",
        " \n",
-       "        [[-2.686432 ]],\n",
+       "        [[14.379672]],\n",
        " \n",
-       "        [[-2.4956915]],\n",
+       "        [[13.368358]],\n",
        " \n",
-       "        [[-2.6606524]],\n",
+       "        [[13.446113]],\n",
        " \n",
-       "        [[-2.825928 ]],\n",
+       "        [[15.118556]],\n",
        " \n",
-       "        [[-2.5841327]],\n",
+       "        [[13.832502]],\n",
        " \n",
-       "        [[-2.8603108]]], dtype=float32)\n",
+       "        [[14.525592]]], dtype=float32)\n",
        " Coordinates:\n",
        "   * time       (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10\n",
        "   * component  (component) object 8B 'random_walk'\n",
@@ -480,7 +475,7 @@
        "     hierarchy:          None]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "id": "dc5901f7",
    "metadata": {},
    "outputs": [],
@@ -521,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "id": "d6546388",
    "metadata": {},
    "outputs": [],
@@ -531,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 11,
    "id": "f712c297-579a-4ede-88a6-198ed7b17ca0",
    "metadata": {
     "ExecuteTime": {
@@ -558,7 +553,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/benjy/projects_dev/ontime/.venv/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     }
@@ -573,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 12,
    "id": "2bb7fe33",
    "metadata": {},
    "outputs": [
@@ -951,80 +946,80 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 10, component: 1, sample: 1)&gt; Size: 80B\n",
-       "array([[[-4.05516413]],\n",
+       "array([[[26.51311037]],\n",
        "\n",
-       "       [[-3.97963323]],\n",
+       "       [[26.79350356]],\n",
        "\n",
-       "       [[-4.70101381]],\n",
+       "       [[27.33226923]],\n",
        "\n",
-       "       [[-3.94403114]],\n",
+       "       [[26.90249441]],\n",
        "\n",
-       "       [[-4.5825427 ]],\n",
+       "       [[27.77318538]],\n",
        "\n",
-       "       [[-3.87290451]],\n",
+       "       [[27.31159983]],\n",
        "\n",
-       "       [[-3.84491919]],\n",
+       "       [[27.34664187]],\n",
        "\n",
-       "       [[-3.65995089]],\n",
+       "       [[26.14925155]],\n",
        "\n",
-       "       [[-4.09419224]],\n",
+       "       [[26.61025269]],\n",
        "\n",
-       "       [[-3.7710456 ]]])\n",
+       "       [[25.49064079]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10\n",
        "  * component  (component) object 8B &#x27;pred&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4556b878-97a1-49f3-b4f9-f98843a3a211' class='xr-array-in' type='checkbox' checked><label for='section-4556b878-97a1-49f3-b4f9-f98843a3a211' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-4.055 -3.98 -4.701 -3.944 -4.583 -3.873 -3.845 -3.66 -4.094 -3.771</span></div><div class='xr-array-data'><pre>array([[[-4.05516413]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3f5060b9-d619-40ee-8443-07196263307d' class='xr-array-in' type='checkbox' checked><label for='section-3f5060b9-d619-40ee-8443-07196263307d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>26.51 26.79 27.33 26.9 27.77 27.31 27.35 26.15 26.61 25.49</span></div><div class='xr-array-data'><pre>array([[[26.51311037]],\n",
        "\n",
-       "       [[-3.97963323]],\n",
+       "       [[26.79350356]],\n",
        "\n",
-       "       [[-4.70101381]],\n",
+       "       [[27.33226923]],\n",
        "\n",
-       "       [[-3.94403114]],\n",
+       "       [[26.90249441]],\n",
        "\n",
-       "       [[-4.5825427 ]],\n",
+       "       [[27.77318538]],\n",
        "\n",
-       "       [[-3.87290451]],\n",
+       "       [[27.31159983]],\n",
        "\n",
-       "       [[-3.84491919]],\n",
+       "       [[27.34664187]],\n",
        "\n",
-       "       [[-3.65995089]],\n",
+       "       [[26.14925155]],\n",
        "\n",
-       "       [[-4.09419224]],\n",
+       "       [[26.61025269]],\n",
        "\n",
-       "       [[-3.7710456 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ec02d8dd-143e-42ba-821e-75a64459bdd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec02d8dd-143e-42ba-821e-75a64459bdd8' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-01 ... 2023-01-10</div><input id='attrs-298f91d5-ce2a-4260-ac87-571670607668' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-298f91d5-ce2a-4260-ac87-571670607668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5398efdb-1314-4d5d-826e-3337eef1d392' class='xr-var-data-in' type='checkbox'><label for='data-5398efdb-1314-4d5d-826e-3337eef1d392' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[25.49064079]]])</pre></div></div></li><li class='xr-section-item'><input id='section-2aecd77d-86e8-4706-bd80-bc97609fed52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2aecd77d-86e8-4706-bd80-bc97609fed52' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-01 ... 2023-01-10</div><input id='attrs-fed10e3e-276d-43e9-a314-4ffa8186d491' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fed10e3e-276d-43e9-a314-4ffa8186d491' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a418ece-d3e7-4abe-969f-772a0c08f494' class='xr-var-data-in' type='checkbox'><label for='data-1a418ece-d3e7-4abe-969f-772a0c08f494' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-03T00:00:00.000000000&#x27;, &#x27;2023-01-04T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-05T00:00:00.000000000&#x27;, &#x27;2023-01-06T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-07T00:00:00.000000000&#x27;, &#x27;2023-01-08T00:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-09T00:00:00.000000000&#x27;, &#x27;2023-01-10T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;pred&#x27;</div><input id='attrs-5a13dc9c-e881-4b25-925e-2e734af88632' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a13dc9c-e881-4b25-925e-2e734af88632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21c4b2f7-7d36-4641-aac4-1e5c880364e2' class='xr-var-data-in' type='checkbox'><label for='data-21c4b2f7-7d36-4641-aac4-1e5c880364e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pred&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-549e87f4-2b0d-430e-91f0-892e97aa2528' class='xr-section-summary-in' type='checkbox'  ><label for='section-549e87f4-2b0d-430e-91f0-892e97aa2528' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6a4f28df-7132-4c44-86e1-e1ca832dfcac' class='xr-index-data-in' type='checkbox'/><label for='index-6a4f28df-7132-4c44-86e1-e1ca832dfcac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-01&#x27;, &#x27;2023-01-02&#x27;, &#x27;2023-01-03&#x27;, &#x27;2023-01-04&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;pred&#x27;</div><input id='attrs-e3a8b909-6769-4fce-8cfa-7a5eb6880776' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3a8b909-6769-4fce-8cfa-7a5eb6880776' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71069a29-8342-4ff8-ac35-24880fe72b67' class='xr-var-data-in' type='checkbox'><label for='data-71069a29-8342-4ff8-ac35-24880fe72b67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pred&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9285f67-b1b2-48a1-8e27-86719fb7db10' class='xr-section-summary-in' type='checkbox'  ><label for='section-e9285f67-b1b2-48a1-8e27-86719fb7db10' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b4448108-15d2-41ab-b171-b9b280286952' class='xr-index-data-in' type='checkbox'/><label for='index-b4448108-15d2-41ab-b171-b9b280286952' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-01&#x27;, &#x27;2023-01-02&#x27;, &#x27;2023-01-03&#x27;, &#x27;2023-01-04&#x27;,\n",
        "               &#x27;2023-01-05&#x27;, &#x27;2023-01-06&#x27;, &#x27;2023-01-07&#x27;, &#x27;2023-01-08&#x27;,\n",
        "               &#x27;2023-01-09&#x27;, &#x27;2023-01-10&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f0c5c9b9-2aae-4e68-9d6f-c6ae7d1d6322' class='xr-index-data-in' type='checkbox'/><label for='index-f0c5c9b9-2aae-4e68-9d6f-c6ae7d1d6322' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pred&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1dca861-a6ed-422a-b0b0-1214cca09b5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1dca861-a6ed-422a-b0b0-1214cca09b5c' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f2f48e30-a32f-4c9e-a57d-b569baf2514d' class='xr-index-data-in' type='checkbox'/><label for='index-f2f48e30-a32f-4c9e-a57d-b569baf2514d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pred&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74200bdd-5fbb-4c67-afd3-044b05eea8e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74200bdd-5fbb-4c67-afd3-044b05eea8e7' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<TimeSeries (DataArray) (time: 10, component: 1, sample: 1)> Size: 80B\n",
-       "array([[[-4.05516413]],\n",
+       "array([[[26.51311037]],\n",
        "\n",
-       "       [[-3.97963323]],\n",
+       "       [[26.79350356]],\n",
        "\n",
-       "       [[-4.70101381]],\n",
+       "       [[27.33226923]],\n",
        "\n",
-       "       [[-3.94403114]],\n",
+       "       [[26.90249441]],\n",
        "\n",
-       "       [[-4.5825427 ]],\n",
+       "       [[27.77318538]],\n",
        "\n",
-       "       [[-3.87290451]],\n",
+       "       [[27.31159983]],\n",
        "\n",
-       "       [[-3.84491919]],\n",
+       "       [[27.34664187]],\n",
        "\n",
-       "       [[-3.65995089]],\n",
+       "       [[26.14925155]],\n",
        "\n",
-       "       [[-4.09419224]],\n",
+       "       [[26.61025269]],\n",
        "\n",
-       "       [[-3.7710456 ]]])\n",
+       "       [[25.49064079]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 80B 2023-01-01 2023-01-02 ... 2023-01-10\n",
        "  * component  (component) object 8B 'pred'\n",
@@ -1034,7 +1029,7 @@
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1063,7 +1058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 13,
    "id": "7bcdf970",
    "metadata": {},
    "outputs": [],
@@ -1073,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 14,
    "id": "e247d997",
    "metadata": {},
    "outputs": [],
@@ -1097,7 +1092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 15,
    "id": "5831e0b0",
    "metadata": {},
    "outputs": [],
@@ -1116,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 16,
    "id": "43bb5fc7",
    "metadata": {},
    "outputs": [
@@ -1130,38 +1125,47 @@
       "lightning.pytorch.utilities.rank_zero INFO  TPU available: False, using: 0 TPU cores\n",
       "INFO: HPU available: False, using: 0 HPUs\n",
       "lightning.pytorch.utilities.rank_zero INFO  HPU available: False, using: 0 HPUs\n",
+      "INFO: You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "lightning.pytorch.utilities.rank_zero INFO  You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "2025-01-06 16:26:59.828777: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2025-01-06 16:27:00.179475: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1736177220.320421  284635 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1736177220.361063  284635 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2025-01-06 16:27:00.703629: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
       "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
       "lightning.pytorch.accelerators.cuda INFO  LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
       "INFO: \n",
       "  | Name    | Type      | Params | Mode \n",
       "----------------------------------------------\n",
       "0 | model   | SimpleGRU | 9.7 K  | train\n",
-      "1 | loss_fn | MSELoss   | 0      | eval \n",
+      "1 | loss_fn | MSELoss   | 0      | train\n",
       "----------------------------------------------\n",
       "9.7 K     Trainable params\n",
       "0         Non-trainable params\n",
       "9.7 K     Total params\n",
       "0.039     Total estimated model params size (MB)\n",
-      "3         Modules in train mode\n",
-      "1         Modules in eval mode\n",
+      "4         Modules in train mode\n",
+      "0         Modules in eval mode\n",
       "lightning.pytorch.callbacks.model_summary INFO  \n",
       "  | Name    | Type      | Params | Mode \n",
       "----------------------------------------------\n",
       "0 | model   | SimpleGRU | 9.7 K  | train\n",
-      "1 | loss_fn | MSELoss   | 0      | eval \n",
+      "1 | loss_fn | MSELoss   | 0      | train\n",
       "----------------------------------------------\n",
       "9.7 K     Trainable params\n",
       "0         Non-trainable params\n",
       "9.7 K     Total params\n",
       "0.039     Total estimated model params size (MB)\n",
-      "3         Modules in train mode\n",
-      "1         Modules in eval mode\n"
+      "4         Modules in train mode\n",
+      "0         Modules in eval mode\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5c907fd97bbf47eda7babf9f8a606fb5",
+       "model_id": "bfb58fca6f114994a2141495ad5cdc8d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1176,13 +1180,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/benjy/projects_dev/ontime/.venv/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (9) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+      "/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (9) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5184826d803e4c76b4797631521e31e3",
+       "model_id": "e6cf0557c7794e5cb12118325340caf7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1196,7 +1200,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "519ac2bcbc3b474aaabfc4072202f3a7",
+       "model_id": "d6e3257d29674f788184d18e25b03d42",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1210,7 +1214,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0711aa056104d20b2350f8d883d72f3",
+       "model_id": "bd3c96b6cd38494c813d280a8f179a4c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1224,7 +1228,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ebec78217c734bffb555ee39b5d8e73a",
+       "model_id": "55f4162221fd4964b337d90ea9b5cefa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1238,7 +1242,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "500d1c70f64249a09d5c8b4b9684450a",
+       "model_id": "7d1c151a5336426887d7559dd60d9486",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1252,7 +1256,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f33b772bc1b543c3a03c5665990a2540",
+       "model_id": "2414cc0b752e4172b6ae56435d23a57c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1266,7 +1270,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7fedb11664146519d2f48abedd9aa61",
+       "model_id": "d4553f0f743a40fd851150610035abbd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1280,7 +1284,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25a4884f2f714c53ac72ea482cf92f26",
+       "model_id": "2b0148e900be44cfa6eae4ecc34416f2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1294,7 +1298,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e697cc666e148f394a920a5961b70f3",
+       "model_id": "7dc63cad79ba4340a8be9f564b7d33c7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1308,7 +1312,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a0d92c2f0f14aff97a6df9a7196d30e",
+       "model_id": "7a66106178064f7ba9c5d7162f92528d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1322,7 +1326,7 @@
     {
      "data": {
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+      "/home/benjy/projects_dev/ontime/src/ontime/core/modelling/libs/pytorch/pytorch_forecasting_model.py:97: UserWarning: The requested prediction horizon (n=10) exceeds the model's output_chunk_length (6). The model will use an iterative forecasting approach, which may result in reduced accuracy due to error propagation.\n",
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       "  warnings.warn(\n",
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        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-2444f6b3006b491d8afba5e617fa386b.vega-embed details,\n",
-       "  #altair-viz-2444f6b3006b491d8afba5e617fa386b.vega-embed details summary {\n",
+       "  #altair-viz-50ad92e7681c4a2fa96a42199a904dce.vega-embed details,\n",
+       "  #altair-viz-50ad92e7681c4a2fa96a42199a904dce.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-2444f6b3006b491d8afba5e617fa386b\"></div>\n",
+       "<div id=\"altair-viz-50ad92e7681c4a2fa96a42199a904dce\"></div>\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-2444f6b3006b491d8afba5e617fa386b\");\n",
+       "    if (outputDiv.id !== \"altair-viz-50ad92e7681c4a2fa96a42199a904dce\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-50ad92e7681c4a2fa96a42199a904dce\");\n",
        "    }\n",
        "\n",
        "    const paths = {\n",
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\"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"title\": \"time\", \"grid\": false, \"labelFlush\": true, \"labelOverlap\": true}, {\"scale\": \"y\", \"labelOverlap\": true, \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"title\": \"value\", \"zindex\": 0, \"orient\": \"left\"}], \"padding\": 5, \"height\": 300, \"background\": \"white\", \"width\": 600, \"style\": \"cell\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}]}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2159,7 +2163,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/user_guide/0_core/0.5_plots.ipynb b/docs/user_guide/0_core/0.5_plots.ipynb
index 6731cbb..3304feb 100644
--- a/docs/user_guide/0_core/0.5_plots.ipynb
+++ b/docs/user_guide/0_core/0.5_plots.ipynb
@@ -60,16 +60,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import ontime as on\n",
@@ -267,28 +258,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-a099bed384274eba917af64a5d4a0c59.vega-embed {\n",
+       "  #altair-viz-351dae3fce13434fa0011a23d7bfcd4f.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-a099bed384274eba917af64a5d4a0c59.vega-embed details,\n",
-       "  #altair-viz-a099bed384274eba917af64a5d4a0c59.vega-embed details summary {\n",
+       "  #altair-viz-351dae3fce13434fa0011a23d7bfcd4f.vega-embed details,\n",
+       "  #altair-viz-351dae3fce13434fa0011a23d7bfcd4f.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-a099bed384274eba917af64a5d4a0c59\"></div>\n",
+       "<div id=\"altair-viz-351dae3fce13434fa0011a23d7bfcd4f\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-a099bed384274eba917af64a5d4a0c59\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-a099bed384274eba917af64a5d4a0c59\");\n",
+       "    if (outputDiv.id !== \"altair-viz-351dae3fce13434fa0011a23d7bfcd4f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-351dae3fce13434fa0011a23d7bfcd4f\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -321,15 +313,16 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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54.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T07:00:00.000\", \"generation biomass\": 359.0, \"generation solar\": 116.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T08:00:00.000\", \"generation biomass\": 357.0, \"generation solar\": 1039.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T09:00:00.000\", \"generation biomass\": 377.0, \"generation solar\": 2373.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T10:00:00.000\", \"generation biomass\": 376.0, \"generation solar\": 3699.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T11:00:00.000\", \"generation biomass\": 376.0, \"generation solar\": 4154.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T12:00:00.000\", \"generation biomass\": 373.0, \"generation solar\": 4074.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T13:00:00.000\", \"generation biomass\": 383.0, \"generation solar\": 3851.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T14:00:00.000\", \"generation biomass\": 391.0, \"generation solar\": 3619.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T15:00:00.000\", \"generation biomass\": 395.0, \"generation solar\": 3024.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T16:00:00.000\", \"generation biomass\": 394.0, \"generation solar\": 2053.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T17:00:00.000\", \"generation biomass\": 410.0, \"generation solar\": 769.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T18:00:00.000\", \"generation biomass\": 435.0, \"generation solar\": 266.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T19:00:00.000\", \"generation biomass\": 435.0, \"generation solar\": 165.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T20:00:00.000\", \"generation biomass\": 437.0, \"generation solar\": 137.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T21:00:00.000\", \"generation biomass\": 437.0, \"generation solar\": 137.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T22:00:00.000\", \"generation biomass\": 443.0, \"generation solar\": 137.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-02T23:00:00.000\", \"generation biomass\": 478.0, \"generation solar\": 62.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T00:00:00.000\", \"generation biomass\": 463.0, \"generation solar\": 62.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T01:00:00.000\", \"generation 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\"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T08:00:00.000\", \"generation biomass\": 433.0, \"generation solar\": 820.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T09:00:00.000\", \"generation biomass\": 419.0, \"generation solar\": 1665.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T10:00:00.000\", \"generation biomass\": 431.0, \"generation solar\": 2488.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T11:00:00.000\", \"generation biomass\": 425.0, \"generation solar\": 3218.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T12:00:00.000\", \"generation biomass\": 427.0, \"generation solar\": 3695.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T13:00:00.000\", \"generation biomass\": 421.0, \"generation solar\": 3780.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T14:00:00.000\", \"generation biomass\": 410.0, \"generation solar\": 3370.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T15:00:00.000\", \"generation biomass\": 397.0, \"generation solar\": 2692.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T16:00:00.000\", \"generation biomass\": 393.0, \"generation solar\": 1931.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T17:00:00.000\", \"generation biomass\": 418.0, \"generation solar\": 1015.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T18:00:00.000\", \"generation biomass\": 458.0, \"generation solar\": 502.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T19:00:00.000\", \"generation biomass\": 462.0, \"generation solar\": 355.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T20:00:00.000\", \"generation biomass\": 462.0, \"generation solar\": 291.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T21:00:00.000\", \"generation biomass\": 463.0, \"generation solar\": 207.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T22:00:00.000\", \"generation biomass\": 456.0, \"generation solar\": 117.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-03T23:00:00.000\", \"generation biomass\": 391.0, \"generation solar\": 692.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T00:00:00.000\", \"generation biomass\": 351.0, \"generation solar\": 684.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T01:00:00.000\", \"generation biomass\": 350.0, \"generation solar\": 648.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T02:00:00.000\", \"generation 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1634.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T09:00:00.000\", \"generation biomass\": 402.0, \"generation solar\": 3830.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T10:00:00.000\", \"generation biomass\": 411.0, \"generation solar\": 4937.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T11:00:00.000\", \"generation biomass\": 403.0, \"generation solar\": 5302.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T12:00:00.000\", \"generation biomass\": 407.0, \"generation solar\": 5590.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T13:00:00.000\", \"generation biomass\": 393.0, \"generation solar\": 5668.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T14:00:00.000\", \"generation biomass\": 400.0, \"generation solar\": 5593.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T15:00:00.000\", \"generation biomass\": 397.0, \"generation solar\": 5316.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T16:00:00.000\", \"generation biomass\": 393.0, \"generation solar\": 4907.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T17:00:00.000\", \"generation biomass\": 402.0, \"generation solar\": 4200.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T18:00:00.000\", \"generation biomass\": 409.0, \"generation solar\": 2911.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T19:00:00.000\", \"generation biomass\": 438.0, \"generation solar\": 1392.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T20:00:00.000\", \"generation biomass\": 436.0, \"generation solar\": 748.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T21:00:00.000\", \"generation biomass\": 424.0, \"generation solar\": 696.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T22:00:00.000\", \"generation biomass\": 403.0, \"generation solar\": 672.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-04T23:00:00.000\", \"generation biomass\": 373.0, \"generation solar\": 691.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T00:00:00.000\", \"generation biomass\": 363.0, \"generation solar\": 668.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T01:00:00.000\", \"generation biomass\": 359.0, \"generation solar\": 662.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T02:00:00.000\", \"generation biomass\": 346.5, \"generation solar\": 616.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T03:00:00.000\", \"generation 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2482.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T10:00:00.000\", \"generation biomass\": 376.0, \"generation solar\": 3339.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T11:00:00.000\", \"generation biomass\": 375.14285714285717, \"generation solar\": 3282.5714285714284, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T12:00:00.000\", \"generation biomass\": 374.2857142857143, \"generation solar\": 3226.1428571428573, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T13:00:00.000\", \"generation biomass\": 373.42857142857144, \"generation solar\": 3169.714285714286, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T14:00:00.000\", \"generation biomass\": 372.57142857142856, \"generation solar\": 3113.285714285714, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T15:00:00.000\", \"generation biomass\": 371.7142857142857, \"generation solar\": 3056.8571428571427, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T16:00:00.000\", \"generation biomass\": 370.85714285714283, \"generation solar\": 3000.4285714285716, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T17:00:00.000\", \"generation biomass\": 370.0, \"generation solar\": 2944.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T18:00:00.000\", \"generation biomass\": 375.0, \"generation solar\": 1971.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T19:00:00.000\", \"generation biomass\": 371.0, \"generation solar\": 958.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T20:00:00.000\", \"generation biomass\": 370.0, \"generation solar\": 605.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T21:00:00.000\", \"generation biomass\": 362.0, \"generation solar\": 518.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T22:00:00.000\", \"generation biomass\": 370.0, \"generation solar\": 394.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-05T23:00:00.000\", \"generation biomass\": 535.0, \"generation solar\": 626.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T00:00:00.000\", \"generation biomass\": 532.0, \"generation solar\": 624.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T01:00:00.000\", \"generation biomass\": 530.0, \"generation solar\": 616.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T02:00:00.000\", \"generation biomass\": 532.0, \"generation solar\": 582.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T03:00:00.000\", \"generation biomass\": 541.0, \"generation solar\": 576.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T04:00:00.000\", \"generation biomass\": 545.0, \"generation solar\": 499.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T05:00:00.000\", \"generation biomass\": 540.0, \"generation solar\": 415.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T06:00:00.000\", \"generation biomass\": 546.0, \"generation solar\": 412.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T07:00:00.000\", \"generation biomass\": 546.0, \"generation solar\": 1168.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T08:00:00.000\", \"generation biomass\": 544.0, \"generation solar\": 3018.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T09:00:00.000\", \"generation biomass\": 543.0, \"generation 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between solar and biomass generation\"}, {\"time\": \"2015-01-06T16:00:00.000\", \"generation biomass\": 532.0, \"generation solar\": 3824.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T17:00:00.000\", \"generation biomass\": 533.0, \"generation solar\": 3132.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T18:00:00.000\", \"generation biomass\": 528.0, \"generation solar\": 2383.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T19:00:00.000\", \"generation biomass\": 536.0, \"generation solar\": 1586.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T20:00:00.000\", \"generation biomass\": 535.0, \"generation solar\": 891.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T21:00:00.000\", \"generation biomass\": 531.0, \"generation solar\": 696.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T22:00:00.000\", \"generation biomass\": 528.0, \"generation solar\": 669.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-06T23:00:00.000\", \"generation biomass\": 499.0, \"generation solar\": 1280.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T00:00:00.000\", \"generation biomass\": 502.0, \"generation solar\": 1253.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T01:00:00.000\", \"generation biomass\": 491.0, \"generation solar\": 1231.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T02:00:00.000\", \"generation biomass\": 495.0, \"generation solar\": 1223.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T03:00:00.000\", \"generation biomass\": 503.0, \"generation solar\": 1189.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T04:00:00.000\", \"generation biomass\": 505.0, \"generation solar\": 1142.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T05:00:00.000\", \"generation biomass\": 506.0, \"generation solar\": 1043.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T06:00:00.000\", \"generation biomass\": 505.0, \"generation solar\": 979.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T07:00:00.000\", \"generation biomass\": 510.0, \"generation solar\": 1336.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T08:00:00.000\", \"generation biomass\": 501.0, \"generation solar\": 2121.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T09:00:00.000\", \"generation biomass\": 494.0, \"generation solar\": 2841.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T10:00:00.000\", \"generation biomass\": 498.0, \"generation solar\": 3556.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T11:00:00.000\", \"generation biomass\": 491.0, \"generation solar\": 3756.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T12:00:00.000\", \"generation biomass\": 495.0, \"generation solar\": 3639.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T13:00:00.000\", \"generation biomass\": 501.0, \"generation solar\": 4133.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T14:00:00.000\", \"generation biomass\": 498.0, \"generation solar\": 4896.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T15:00:00.000\", \"generation biomass\": 491.0, \"generation solar\": 4647.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T16:00:00.000\", \"generation biomass\": 495.0, \"generation solar\": 4184.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T17:00:00.000\", \"generation biomass\": 497.0, \"generation solar\": 3631.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T18:00:00.000\", \"generation biomass\": 483.0, \"generation solar\": 2772.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T19:00:00.000\", \"generation biomass\": 482.0, \"generation solar\": 2013.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T20:00:00.000\", \"generation biomass\": 481.0, \"generation solar\": 1193.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T21:00:00.000\", \"generation biomass\": 481.0, \"generation solar\": 636.0, \"area\": \"Diff. between solar and biomass generation\"}, {\"time\": \"2015-01-07T22:00:00.000\", \"generation biomass\": 479.0, \"generation solar\": 573.0, \"area\": \"Diff. between solar and biomass 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@@ -1058,7 +1063,7 @@
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-       "  #altair-viz-5bf85cf9e64b402d8311fae5132da84d.vega-embed details summary {\n",
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-       "<div id=\"altair-viz-5bf85cf9e64b402d8311fae5132da84d\"></div>\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-5bf85cf9e64b402d8311fae5132da84d\");\n",
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-     "execution_count": 23,
+     "execution_count": 16,
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@@ -1187,7 +1194,7 @@
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+   "execution_count": 17,
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@@ -1196,28 +1203,29 @@
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        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
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-       "  #altair-viz-d242739f633a4a7d94cbf5e1f40b0f51.vega-embed details summary {\n",
+       "  #altair-viz-989266f158b34a15b9daf66a74c8ce66.vega-embed details,\n",
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        "</style>\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-d242739f633a4a7d94cbf5e1f40b0f51\");\n",
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+       "      outputDiv = document.getElementById(\"altair-viz-989266f158b34a15b9daf66a74c8ce66\");\n",
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+       "\n",
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        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
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        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
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        "        .catch(showError)\n",
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-     "execution_count": 24,
+     "execution_count": 17,
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@@ -1304,7 +1313,7 @@
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+   "execution_count": 18,
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@@ -1314,7 +1323,7 @@
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+   "execution_count": 19,
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@@ -1324,17 +1333,17 @@
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+   "execution_count": 20,
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     {
      "data": {
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-       "<ontime.context.common.generic_predictor.GenericPredictor at 0x1759654b0>"
+       "<ontime.context.common.generic_predictor.GenericPredictor at 0x7f95d3d5b010>"
       ]
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-     "execution_count": 27,
+     "execution_count": 20,
      "metadata": {},
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@@ -1346,7 +1355,7 @@
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+   "execution_count": 21,
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@@ -1356,7 +1365,7 @@
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+   "execution_count": 22,
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@@ -1376,7 +1385,7 @@
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+   "execution_count": 23,
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    "metadata": {},
    "outputs": [
@@ -1385,28 +1394,29 @@
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-       "  #altair-viz-f38eaba7793c4d13b4b2d7401749da0e.vega-embed {\n",
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        "    width: 100%;\n",
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        "  }\n",
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-       "  #altair-viz-f38eaba7793c4d13b4b2d7401749da0e.vega-embed details,\n",
-       "  #altair-viz-f38eaba7793c4d13b4b2d7401749da0e.vega-embed details summary {\n",
+       "  #altair-viz-d35274dde9c64d1f91464ea75e38ffd1.vega-embed details,\n",
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        "</style>\n",
-       "<div id=\"altair-viz-f38eaba7793c4d13b4b2d7401749da0e\"></div>\n",
+       "<div id=\"altair-viz-d35274dde9c64d1f91464ea75e38ffd1\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-f38eaba7793c4d13b4b2d7401749da0e\");\n",
+       "    if (outputDiv.id !== \"altair-viz-d35274dde9c64d1f91464ea75e38ffd1\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-d35274dde9c64d1f91464ea75e38ffd1\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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@@ -1439,22 +1449,23 @@
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        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
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        "        .catch(showError)\n",
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\"width\": 600, \"height\": 200}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1488,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 24,
    "id": "0bea5b4f-e11b-4836-a039-d61117a4684d",
    "metadata": {},
    "outputs": [],
@@ -1508,7 +1519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 25,
    "id": "e5c7dba4-1ff8-47c9-907b-4bde47ca09dc",
    "metadata": {},
    "outputs": [],
@@ -1542,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 26,
    "id": "9e89c5ca-3456-446c-8b5e-507574f43df5",
    "metadata": {},
    "outputs": [],
@@ -1552,7 +1563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 27,
    "id": "03364cbd-61aa-4660-9789-ab7f4662ab79",
    "metadata": {},
    "outputs": [],
@@ -1571,7 +1582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 28,
    "id": "4f810223-cfd2-429b-8d7d-6182c26d7246",
    "metadata": {},
    "outputs": [],
@@ -1586,14 +1597,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 29,
    "id": "d0d58fb4-2104-444c-9385-6597beb81a6d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ts_ano_point = ts_ano_point.rename({'0': 'Ponctual anomalies'})\n",
-    "ts_ano_collective = ts_ano_collective.rename({'0': 'Collective anomalies'})\n",
-    "ts_ano_contextual = ts_ano_contextual.rename({'0': 'Contextual anomalies'})"
+    "ts_ano_point = ts_ano_point.rename({'generation biomass': 'Ponctual anomalies'})\n",
+    "ts_ano_collective = ts_ano_collective.rename({'generation biomass': 'Collective anomalies'})\n",
+    "ts_ano_contextual = ts_ano_contextual.rename({'generation biomass': 'Contextual anomalies'})"
    ]
   },
   {
@@ -1606,20 +1617,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 82,
    "id": "2e944b87-b85d-4c5c-b963-857f79e6b2a4",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define windows for plotting\n",
-    "start = 24 * 7 * 4 * 9\n",
-    "duration = 24 * 7 * 10\n",
+    "start = 24 * 7 * 54\n",
+    "duration = 24 * 7 * 15\n",
     "end = start + duration"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 83,
    "id": "d4e93bfd-5e3e-40d4-9c45-577eff06579a",
    "metadata": {},
    "outputs": [
@@ -1628,28 +1639,29 @@
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        "\n",
        "<style>\n",
-       "  #altair-viz-4c30832b941a48c7a0404a5aaccf9549.vega-embed {\n",
+       "  #altair-viz-e2b2d20c239243a58f09f86cfc55d167.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-4c30832b941a48c7a0404a5aaccf9549.vega-embed details,\n",
-       "  #altair-viz-4c30832b941a48c7a0404a5aaccf9549.vega-embed details summary {\n",
+       "  #altair-viz-e2b2d20c239243a58f09f86cfc55d167.vega-embed details,\n",
+       "  #altair-viz-e2b2d20c239243a58f09f86cfc55d167.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-4c30832b941a48c7a0404a5aaccf9549\"></div>\n",
+       "<div id=\"altair-viz-e2b2d20c239243a58f09f86cfc55d167\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-4c30832b941a48c7a0404a5aaccf9549\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-4c30832b941a48c7a0404a5aaccf9549\");\n",
+       "    if (outputDiv.id !== \"altair-viz-e2b2d20c239243a58f09f86cfc55d167\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-e2b2d20c239243a58f09f86cfc55d167\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -1682,22 +1694,23 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1725,7 +1738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 84,
    "id": "af517b10-9441-4d17-9664-6eb35e4afef3",
    "metadata": {},
    "outputs": [],
@@ -1737,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 85,
    "id": "0a73d11f-4b21-45a8-8101-79fa3105d293",
    "metadata": {},
    "outputs": [],
@@ -1756,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 86,
    "id": "3a12d720-4522-4aac-ae54-5975b52848aa",
    "metadata": {},
    "outputs": [
@@ -1765,28 +1778,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c.vega-embed {\n",
+       "  #altair-viz-32ab6d3d33bc4222abe064ec52493b8c.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c.vega-embed details,\n",
-       "  #altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c.vega-embed details summary {\n",
+       "  #altair-viz-32ab6d3d33bc4222abe064ec52493b8c.vega-embed details,\n",
+       "  #altair-viz-32ab6d3d33bc4222abe064ec52493b8c.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c\"></div>\n",
+       "<div id=\"altair-viz-32ab6d3d33bc4222abe064ec52493b8c\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-8e8c16b2563a41e38f5d4b28c9983b5c\");\n",
+       "    if (outputDiv.id !== \"altair-viz-32ab6d3d33bc4222abe064ec52493b8c\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-32ab6d3d33bc4222abe064ec52493b8c\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -1819,22 +1833,23 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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\"Measurement\", \"value\": 4.152540147191631}, {\"time\": \"2022-05-02T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.027003440695241}, {\"time\": \"2022-05-03T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.175837172628549}, {\"time\": \"2022-05-04T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.144335207450408}, {\"time\": \"2022-05-05T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.8181293079955174}, {\"time\": \"2022-05-06T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.7682871222295247}, {\"time\": \"2022-05-07T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.554311664616452}, {\"time\": \"2022-05-08T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.8151069114354073}, {\"time\": \"2022-05-09T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.3928367722438546}, {\"time\": \"2022-05-10T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.407864950083659}, {\"time\": \"2022-05-11T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.380820205095896}, {\"time\": \"2022-05-12T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.428229883156333}, {\"time\": \"2022-05-13T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 5.04170792082734}, {\"time\": \"2022-05-14T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.095651947148574}, {\"time\": \"2022-05-15T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.656909570373405}, {\"time\": \"2022-05-16T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.6813928676186523}, {\"time\": \"2022-05-17T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.155221649306288}, {\"time\": \"2022-05-18T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.8742749247370307}, {\"time\": \"2022-05-19T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.047994511928233}, {\"time\": \"2022-05-20T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.8982192283961914}, {\"time\": \"2022-05-21T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.5101426521229038}, {\"time\": \"2022-05-22T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.142814405962156}, {\"time\": \"2022-05-23T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.3810322492274199}, {\"time\": \"2022-05-24T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.0603205306269439}, {\"time\": \"2022-05-25T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 0.5322437807226779}, {\"time\": \"2022-05-26T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 0.5343484140148883}, {\"time\": \"2022-05-27T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.280574994981017}, {\"time\": \"2022-05-28T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 0.5989980586822838}, {\"time\": \"2022-05-29T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.5586542581797393}, {\"time\": \"2022-05-30T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.949307632616053}, {\"time\": \"2022-05-31T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.494563306298872}, {\"time\": \"2022-06-01T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.231883003075834}, {\"time\": \"2022-06-02T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.6587664265404678}, {\"time\": \"2022-06-03T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.9626634718941927}, {\"time\": \"2022-06-04T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.207826539469804}, {\"time\": \"2022-06-05T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.3895624907547663}, {\"time\": \"2022-06-06T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.9299975274537826}, {\"time\": \"2022-06-07T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.965292338778003}, {\"time\": \"2022-06-08T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.3401643475845564}, {\"time\": \"2022-06-09T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.7726285822434975}, {\"time\": \"2022-06-10T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.140870819791596}, {\"time\": \"2022-06-11T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.7574740527134494}, {\"time\": \"2022-06-12T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.448409132535475}, {\"time\": \"2022-06-13T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.346336339761629}, {\"time\": \"2022-06-14T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.0666404763648085}, {\"time\": \"2022-06-15T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.0257312455333527}, {\"time\": \"2022-06-16T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.0635767092964623}, {\"time\": \"2022-06-17T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.744617306331921}, {\"time\": \"2022-06-18T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.7949592762208901}, {\"time\": \"2022-06-19T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.3652788417740394}, {\"time\": \"2022-06-20T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.6928402024528741}, {\"time\": \"2022-06-21T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.7231983714035715}, {\"time\": \"2022-06-22T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.4374260771724003}, {\"time\": \"2022-06-23T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.0333274592291586}, {\"time\": \"2022-06-24T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.2680200882439845}, {\"time\": \"2022-06-25T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.15347451804794}, {\"time\": \"2022-06-26T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 1.5032923304524293}, {\"time\": \"2022-06-27T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.1453626880814283}, {\"time\": \"2022-06-28T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.5002117449755197}, {\"time\": \"2022-06-29T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.592312134301231}, {\"time\": \"2022-06-30T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.995988096368605}, {\"time\": \"2022-07-01T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.4453494029724583}, {\"time\": \"2022-07-02T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 2.4569677471584885}, {\"time\": \"2022-07-03T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.0104679425562093}, {\"time\": \"2022-07-04T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.020252257535605}, {\"time\": \"2022-07-05T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.294852790051446}, {\"time\": \"2022-07-06T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.045953901157745}, {\"time\": \"2022-07-07T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.720136194314634}, {\"time\": \"2022-07-08T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.552416662339179}, {\"time\": \"2022-07-09T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.401851614345882}, {\"time\": \"2022-07-10T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.826618529045055}, {\"time\": \"2022-07-11T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.391609512026017}, {\"time\": \"2022-07-12T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.221303258928716}, {\"time\": \"2022-07-13T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.600171427768398}, {\"time\": \"2022-07-14T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.881362971959297}, {\"time\": \"2022-07-15T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.002062328076227}, {\"time\": \"2022-07-16T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.702365500321398}, {\"time\": \"2022-07-17T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 3.9423701087798215}, {\"time\": \"2022-07-18T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 4.8917181782918195}, {\"time\": \"2022-07-19T00:00:00.000\", \"variable\": \"Measurement\", \"value\": 5.674035078844743}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_area_0\", \"values\": [{\"area\": \"Confidence interval\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"Measurement\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"area\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"area\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"opacity\": {\"value\": 0.6}, \"fill\": {\"field\": \"area\", \"scale\": \"color\"}, \"y2\": {\"field\": \"CI Lower bound\", \"scale\": \"y\"}, \"orient\": {\"value\": \"vertical\"}, \"y\": {\"field\": \"CI Upper bound\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"CI Upper bound\\\"]) && isFinite(+datum[\\\"CI Upper bound\\\"])\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"area\"]}]}, {\"type\": \"group\", \"name\": \"layer_1_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_1\", \"name\": \"faceted_path_layer_1_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"faceted_path_layer_1_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"CI Upper bound\"}, {\"data\": \"source_0\", \"field\": \"CI Lower bound\"}, {\"data\": \"source_1\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_area_0\", \"field\": \"area\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"minExtent\": 0, \"ticks\": false, \"gridScale\": \"y\", \"orient\": \"bottom\", \"aria\": false, \"zindex\": 0, \"labels\": false, \"domain\": false, \"maxExtent\": 0, \"grid\": true}, {\"scale\": \"y\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"domain\": false, \"labels\": false, \"minExtent\": 0, \"gridScale\": \"x\", \"ticks\": false, \"zindex\": 0, \"grid\": true, \"orient\": \"left\", \"maxExtent\": 0, \"aria\": false}, {\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"orient\": \"bottom\", \"labelOverlap\": true, \"title\": \"time\", \"grid\": false, \"labelFlush\": true}, {\"scale\": \"y\", \"labelOverlap\": true, \"zindex\": 0, \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"title\": \"CI Upper bound, CI Lower bound, value\", \"orient\": \"left\"}], \"padding\": 5, \"width\": 600, \"style\": \"cell\", \"legends\": [{\"title\": \"variable\", \"fill\": \"color\", \"symbolType\": \"circle\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.6}}}}, \"stroke\": \"color\"}], \"height\": 200, \"background\": \"white\"}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1869,7 +1884,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1883,7 +1898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/0.6_processors.ipynb b/docs/user_guide/0_core/0.6_processors.ipynb
index 2629393..fe02d16 100644
--- a/docs/user_guide/0_core/0.6_processors.ipynb
+++ b/docs/user_guide/0_core/0.6_processors.ipynb
@@ -72,22 +72,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import ontime as on"
@@ -233,13 +218,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -284,7 +270,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -292,7 +278,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -304,6 +292,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -325,7 +317,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -336,7 +328,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -408,15 +400,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -566,7 +558,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt; Size: 3kB\n",
        "array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
@@ -609,12 +601,12 @@
        "\n",
        "       [[2.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1466f158-f004-4549-a119-034af45476aa' class='xr-array-in' type='checkbox' checked><label for='section-1466f158-f004-4549-a119-034af45476aa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0</span></div><div class='xr-array-data'><pre>array([[[2.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1b5f7259-c995-46ee-9d45-27b6ef258fc2' class='xr-array-in' type='checkbox' checked><label for='section-1b5f7259-c995-46ee-9d45-27b6ef258fc2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0</span></div><div class='xr-array-data'><pre>array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
        "\n",
@@ -654,20 +646,20 @@
        "\n",
        "       [[2.]],\n",
        "\n",
-       "       [[2.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-855d5aa4-3ff9-4dec-84e4-3f2ee589e6a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-855d5aa4-3ff9-4dec-84e4-3f2ee589e6a0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-1c6cbe07-d3cb-4f5a-9b4f-06c949a3256d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c6cbe07-d3cb-4f5a-9b4f-06c949a3256d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f569115e-1979-4432-aa8d-52312eb3e07e' class='xr-var-data-in' type='checkbox'><label for='data-f569115e-1979-4432-aa8d-52312eb3e07e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[2.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ca025d0f-dcc6-4b92-8279-cdec90b1c294' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca025d0f-dcc6-4b92-8279-cdec90b1c294' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-22b574f1-cea7-45fa-b63e-bebe483e2249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22b574f1-cea7-45fa-b63e-bebe483e2249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b5bebf3-cdb9-44ad-8cc7-4411ba25b74c' class='xr-var-data-in' type='checkbox'><label for='data-9b5bebf3-cdb9-44ad-8cc7-4411ba25b74c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-03T00:00:00.000000000&#x27;, ..., &#x27;2022-12-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-12-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-4149e11d-9be9-4f96-a39e-97699eba2e9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4149e11d-9be9-4f96-a39e-97699eba2e9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b883ff1-9106-4ecc-b19e-a8a1a70fea91' class='xr-var-data-in' type='checkbox'><label for='data-7b883ff1-9106-4ecc-b19e-a8a1a70fea91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-488e8c0f-e1e2-4cdc-84ef-73e02c3e3520' class='xr-section-summary-in' type='checkbox'  ><label for='section-488e8c0f-e1e2-4cdc-84ef-73e02c3e3520' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-290f8b2c-2854-461a-9472-95b595b0c3c7' class='xr-index-data-in' type='checkbox'/><label for='index-290f8b2c-2854-461a-9472-95b595b0c3c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-a9378773-3a97-4f97-b280-b35968d4c29d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9378773-3a97-4f97-b280-b35968d4c29d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc96c093-37fb-4173-bb17-f6335dc7b0f4' class='xr-var-data-in' type='checkbox'><label for='data-dc96c093-37fb-4173-bb17-f6335dc7b0f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a523932-2d21-4918-96d9-2976c83d0c37' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a523932-2d21-4918-96d9-2976c83d0c37' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-58fa88ab-80f3-4e53-858b-40366223ee65' class='xr-index-data-in' type='checkbox'/><label for='index-58fa88ab-80f3-4e53-858b-40366223ee65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
        "               &#x27;2022-01-05&#x27;, &#x27;2022-01-06&#x27;, &#x27;2022-01-07&#x27;, &#x27;2022-01-08&#x27;,\n",
        "               &#x27;2022-01-09&#x27;, &#x27;2022-01-10&#x27;,\n",
        "               ...\n",
        "               &#x27;2022-12-22&#x27;, &#x27;2022-12-23&#x27;, &#x27;2022-12-24&#x27;, &#x27;2022-12-25&#x27;,\n",
        "               &#x27;2022-12-26&#x27;, &#x27;2022-12-27&#x27;, &#x27;2022-12-28&#x27;, &#x27;2022-12-29&#x27;,\n",
        "               &#x27;2022-12-30&#x27;, &#x27;2022-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ec79f9b-5a95-4882-b253-fd88606680e2' class='xr-index-data-in' type='checkbox'/><label for='index-2ec79f9b-5a95-4882-b253-fd88606680e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-edf34db8-a874-4841-bcdb-65e08bc57791' class='xr-section-summary-in' type='checkbox'  checked><label for='section-edf34db8-a874-4841-bcdb-65e08bc57791' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d54cc78e-7373-4c78-adc9-83c722cf89e1' class='xr-index-data-in' type='checkbox'/><label for='index-d54cc78e-7373-4c78-adc9-83c722cf89e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7c27e6d-b345-4c86-a931-abcb7367d601' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7c27e6d-b345-4c86-a931-abcb7367d601' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)> Size: 3kB\n",
        "array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
@@ -710,8 +702,8 @@
        "\n",
        "       [[2.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -804,13 +796,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -855,7 +848,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -863,7 +856,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -875,6 +870,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -896,7 +895,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -907,7 +906,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -979,15 +978,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1137,7 +1136,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt; Size: 3kB\n",
        "array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
@@ -1180,12 +1179,12 @@
        "\n",
        "       [[2.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fca8078e-ad4a-4b46-a54b-587cdc631157' class='xr-array-in' type='checkbox' checked><label for='section-fca8078e-ad4a-4b46-a54b-587cdc631157' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0</span></div><div class='xr-array-data'><pre>array([[[2.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a632d462-8ff1-423a-b443-54e7d6228bde' class='xr-array-in' type='checkbox' checked><label for='section-a632d462-8ff1-423a-b443-54e7d6228bde' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0</span></div><div class='xr-array-data'><pre>array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
        "\n",
@@ -1225,20 +1224,20 @@
        "\n",
        "       [[2.]],\n",
        "\n",
-       "       [[2.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-5c8b7b6d-a6be-4dc3-b613-97657ae6bdf1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c8b7b6d-a6be-4dc3-b613-97657ae6bdf1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-927ed53e-8c81-4e5f-a6e0-d02de6f0b80e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-927ed53e-8c81-4e5f-a6e0-d02de6f0b80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6ee0268-4c0a-4e34-938b-5342be59979c' class='xr-var-data-in' type='checkbox'><label for='data-f6ee0268-4c0a-4e34-938b-5342be59979c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[2.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-95dd7945-c8ae-4a29-b07d-fc1c37154f87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95dd7945-c8ae-4a29-b07d-fc1c37154f87' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-d6fb53d3-3e49-49cf-b024-4811c1bc59e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6fb53d3-3e49-49cf-b024-4811c1bc59e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3901df8-65fd-4eaa-b172-46be4478f304' class='xr-var-data-in' type='checkbox'><label for='data-c3901df8-65fd-4eaa-b172-46be4478f304' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-03T00:00:00.000000000&#x27;, ..., &#x27;2022-12-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-12-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-6908af79-4ca2-4cda-8c66-f9a44c1a9812' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6908af79-4ca2-4cda-8c66-f9a44c1a9812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e42fe08-1e53-431a-979a-9b31d97f4d49' class='xr-var-data-in' type='checkbox'><label for='data-7e42fe08-1e53-431a-979a-9b31d97f4d49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd9f1df6-3093-4ae0-841f-e1d763aff7af' class='xr-section-summary-in' type='checkbox'  ><label for='section-bd9f1df6-3093-4ae0-841f-e1d763aff7af' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e486063-e02c-4869-9cef-4e9b5383afb0' class='xr-index-data-in' type='checkbox'/><label for='index-1e486063-e02c-4869-9cef-4e9b5383afb0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-fa906ebd-105c-4724-ab3e-652d734c0b27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa906ebd-105c-4724-ab3e-652d734c0b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dfdead2-764e-486d-ad28-9b7a93ebd04f' class='xr-var-data-in' type='checkbox'><label for='data-8dfdead2-764e-486d-ad28-9b7a93ebd04f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4492970-f6b7-4a47-bb79-464550029118' class='xr-section-summary-in' type='checkbox'  ><label for='section-d4492970-f6b7-4a47-bb79-464550029118' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-00e43a55-c3aa-43f2-a38c-565a394e15f2' class='xr-index-data-in' type='checkbox'/><label for='index-00e43a55-c3aa-43f2-a38c-565a394e15f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
        "               &#x27;2022-01-05&#x27;, &#x27;2022-01-06&#x27;, &#x27;2022-01-07&#x27;, &#x27;2022-01-08&#x27;,\n",
        "               &#x27;2022-01-09&#x27;, &#x27;2022-01-10&#x27;,\n",
        "               ...\n",
        "               &#x27;2022-12-22&#x27;, &#x27;2022-12-23&#x27;, &#x27;2022-12-24&#x27;, &#x27;2022-12-25&#x27;,\n",
        "               &#x27;2022-12-26&#x27;, &#x27;2022-12-27&#x27;, &#x27;2022-12-28&#x27;, &#x27;2022-12-29&#x27;,\n",
        "               &#x27;2022-12-30&#x27;, &#x27;2022-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-928f483e-18b6-4b32-9374-ae72869c15ec' class='xr-index-data-in' type='checkbox'/><label for='index-928f483e-18b6-4b32-9374-ae72869c15ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ae14070-af4f-487a-9738-d1f708bfbba6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ae14070-af4f-487a-9738-d1f708bfbba6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-41e6e0ad-5169-48ce-988a-3d46c11778c5' class='xr-index-data-in' type='checkbox'/><label for='index-41e6e0ad-5169-48ce-988a-3d46c11778c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94018d38-e588-4f9f-b45b-15ce587d2549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94018d38-e588-4f9f-b45b-15ce587d2549' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)> Size: 3kB\n",
        "array([[[2.]],\n",
        "\n",
        "       [[2.]],\n",
@@ -1281,8 +1280,8 @@
        "\n",
        "       [[2.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -1356,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "9bd8cc70-d177-4a05-b153-4f3030c87fad",
    "metadata": {
     "ExecuteTime": {
@@ -1380,12 +1379,12 @@
    },
    "outputs": [],
    "source": [
-    "new_ts = mean.process(ts)"
+    "new_ts = on.TimeSeries.from_darts(mean.process(ts))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "6334dc30-13e5-4050-b74a-1334f7a30217",
    "metadata": {
     "ExecuteTime": {
@@ -1410,23 +1409,83 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-a964c28882d348e393336e2a5eacd647.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-a964c28882d348e393336e2a5eacd647.vega-embed details,\n",
+       "  #altair-viz-a964c28882d348e393336e2a5eacd647.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-a964c28882d348e393336e2a5eacd647\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-a964c28882d348e393336e2a5eacd647\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-a964c28882d348e393336e2a5eacd647\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.3332030390686284}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.7803536513431084}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.8439553522507817}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.28579394967822}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.34695376273144296}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.22437678541371942}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.8936352916620481}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.8010692830516005}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.4846773130258596}, {\"time\": \"2022-01-10T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.05978419159077136}, {\"time\": \"2022-01-11T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.5636242726140934}, {\"time\": \"2022-01-12T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.1323685631000454}, {\"time\": \"2022-01-13T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.7883449048989035}, {\"time\": \"2022-01-14T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -1.12097966751167}, {\"time\": \"2022-01-15T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.9238316453973404}, {\"time\": \"2022-01-16T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.080504416881971}, {\"time\": \"2022-01-17T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -5.20788742710992}, {\"time\": \"2022-01-18T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -4.121979566211303}, {\"time\": \"2022-01-19T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -5.366250531954405}, {\"time\": \"2022-01-20T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -4.737338889612918}, {\"time\": \"2022-01-21T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.1404336848123195}, {\"time\": \"2022-01-22T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.12935585269616956}, {\"time\": \"2022-01-23T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.3216299973435318}, {\"time\": \"2022-01-24T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.6967490202927241}, {\"time\": \"2022-01-25T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.5877866958848725}, {\"time\": \"2022-01-26T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.3192179005739122}, {\"time\": \"2022-01-27T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.0228183096845846}, {\"time\": \"2022-01-28T00:00:00.000\", \"variable\": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1435,7 +1494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "68c344c5-62d4-4157-8cf0-6bb114095487",
    "metadata": {
     "ExecuteTime": {
@@ -1460,23 +1519,83 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-0a87fa62e6724011835af88f6ba6c1be.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-0a87fa62e6724011835af88f6ba6c1be.vega-embed details,\n",
+       "  #altair-viz-0a87fa62e6724011835af88f6ba6c1be.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-0a87fa62e6724011835af88f6ba6c1be\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-0a87fa62e6724011835af88f6ba6c1be\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-0a87fa62e6724011835af88f6ba6c1be\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.3332030390686284}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.5567783452058684}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.6525040142208395}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.5608264980851847}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.5180519510144362}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.3943138282764103}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"rolling_mean_10_random_walk\", \"value\": 0.21032109685663056}, {\"time\": \"2022-01-08T00:00:00.000\", 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1503,7 +1622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "9802955b-6791-43bb-80d4-74ccb4119d70",
    "metadata": {
     "ExecuteTime": {
@@ -1532,7 +1651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "de2ab66c-2832-4007-91d0-f54c427a08e0",
    "metadata": {
     "ExecuteTime": {
@@ -1557,23 +1676,83 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-5f12074ac2df496086523516ad431dc5.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-5f12074ac2df496086523516ad431dc5.vega-embed details,\n",
+       "  #altair-viz-5f12074ac2df496086523516ad431dc5.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-5f12074ac2df496086523516ad431dc5\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-5f12074ac2df496086523516ad431dc5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-5f12074ac2df496086523516ad431dc5\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.6818020821944049}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.6631374314455007}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.0083300896449052}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.6188259194035437}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.525921890896751}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.0377330373629094}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -5.128621433387706}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.2393317351081645}, {\"time\": \"2022-01-09T00:00:00.000\", 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+       "</script>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='time'>"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1616,7 +1795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "7fb009b3-9619-4f68-b04b-4456f915e677",
    "metadata": {
     "ExecuteTime": {
@@ -1638,14 +1817,23 @@
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/benjy/projects_dev/ontime/src/ontime/core/time_series/time_series.py:57: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+      "  splits_df = [g for n, g in df.groupby(pd.Grouper(freq=period))]\n"
+     ]
+    }
+   ],
    "source": [
     "seq = ts.split_by_period('M')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "196aaa0b-ec20-4117-91d2-c0e74387c7e8",
    "metadata": {
     "ExecuteTime": {
@@ -1674,7 +1862,7 @@
        "12"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1702,7 +1890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "f475c77b-bd6a-44e2-bd85-c5d765722667",
    "metadata": {
     "ExecuteTime": {
@@ -1731,7 +1919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "id": "2fd2369b-df3e-4f59-b940-a034216474f4",
    "metadata": {
     "ExecuteTime": {
@@ -1787,13 +1975,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1838,7 +2027,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1846,7 +2035,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1858,6 +2049,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1879,7 +2074,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1890,7 +2085,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1962,15 +2157,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -2120,159 +2315,159 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt;\n",
-       "array([[[ 1.09751547e+00]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 365, component: 1, sample: 1)&gt; Size: 3kB\n",
+       "array([[[  0.68180208]],\n",
        "\n",
-       "       [[ 1.12207918e+00]],\n",
+       "       [[ -0.66313743]],\n",
        "\n",
-       "       [[ 1.40083324e+00]],\n",
+       "       [[ -2.00833009]],\n",
        "\n",
-       "       [[ 9.47470373e-01]],\n",
+       "       [[ -2.61882592]],\n",
        "\n",
-       "       [[ 4.73560549e-01]],\n",
+       "       [[ -2.52592189]],\n",
        "\n",
-       "       [[ 9.04460441e-02]],\n",
+       "       [[ -3.03773304]],\n",
        "\n",
-       "       [[-7.44907944e-01]],\n",
+       "       [[ -5.12862143]],\n",
        "\n",
-       "       [[ 5.22522813e-01]],\n",
+       "       [[ -3.23933174]],\n",
        "\n",
-       "       [[-5.68699769e-01]],\n",
+       "       [[ -3.61242573]],\n",
        "\n",
-       "       [[-3.18083709e-01]],\n",
+       "       [[ -3.38090137]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[-5.26996197e+00]],\n",
+       "       [[-22.67596749]],\n",
        "\n",
-       "       [[-5.57221888e+00]],\n",
+       "       [[-22.40138059]],\n",
        "\n",
-       "       [[-5.85402550e+00]],\n",
+       "       [[-22.28243816]],\n",
        "\n",
-       "       [[-7.94180407e+00]],\n",
+       "       [[-22.55935878]],\n",
        "\n",
-       "       [[-7.52069468e+00]],\n",
+       "       [[-21.24834984]],\n",
        "\n",
-       "       [[-6.39405907e+00]],\n",
+       "       [[-21.00464561]],\n",
        "\n",
-       "       [[-7.71361505e+00]],\n",
+       "       [[-21.78252264]],\n",
        "\n",
-       "       [[-7.92228558e+00]],\n",
+       "       [[-22.49716263]],\n",
        "\n",
-       "       [[-6.68451932e+00]],\n",
+       "       [[-22.44949084]],\n",
        "\n",
-       "       [[-6.94188810e+00]]])\n",
+       "       [[-21.68413141]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b46f3b07-bb60-4f3b-923c-76ed50466868' class='xr-array-in' type='checkbox' checked><label for='section-b46f3b07-bb60-4f3b-923c-76ed50466868' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.098 1.122 1.401 0.9475 0.4736 ... -6.394 -7.714 -7.922 -6.685 -6.942</span></div><div class='xr-array-data'><pre>array([[[ 1.09751547e+00]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-15fa1a74-4888-4a6b-b4bf-007f9643165d' class='xr-array-in' type='checkbox' checked><label for='section-15fa1a74-4888-4a6b-b4bf-007f9643165d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.6818 -0.6631 -2.008 -2.619 -2.526 ... -21.78 -22.5 -22.45 -21.68</span></div><div class='xr-array-data'><pre>array([[[  0.68180208]],\n",
        "\n",
-       "       [[ 1.12207918e+00]],\n",
+       "       [[ -0.66313743]],\n",
        "\n",
-       "       [[ 1.40083324e+00]],\n",
+       "       [[ -2.00833009]],\n",
        "\n",
-       "       [[ 9.47470373e-01]],\n",
+       "       [[ -2.61882592]],\n",
        "\n",
-       "       [[ 4.73560549e-01]],\n",
+       "       [[ -2.52592189]],\n",
        "\n",
-       "       [[ 9.04460441e-02]],\n",
+       "       [[ -3.03773304]],\n",
        "\n",
-       "       [[-7.44907944e-01]],\n",
+       "       [[ -5.12862143]],\n",
        "\n",
-       "       [[ 5.22522813e-01]],\n",
+       "       [[ -3.23933174]],\n",
        "\n",
-       "       [[-5.68699769e-01]],\n",
+       "       [[ -3.61242573]],\n",
        "\n",
-       "       [[-3.18083709e-01]],\n",
+       "       [[ -3.38090137]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[-5.26996197e+00]],\n",
+       "       [[-22.67596749]],\n",
        "\n",
-       "       [[-5.57221888e+00]],\n",
+       "       [[-22.40138059]],\n",
        "\n",
-       "       [[-5.85402550e+00]],\n",
+       "       [[-22.28243816]],\n",
        "\n",
-       "       [[-7.94180407e+00]],\n",
+       "       [[-22.55935878]],\n",
        "\n",
-       "       [[-7.52069468e+00]],\n",
+       "       [[-21.24834984]],\n",
        "\n",
-       "       [[-6.39405907e+00]],\n",
+       "       [[-21.00464561]],\n",
        "\n",
-       "       [[-7.71361505e+00]],\n",
+       "       [[-21.78252264]],\n",
        "\n",
-       "       [[-7.92228558e+00]],\n",
+       "       [[-22.49716263]],\n",
        "\n",
-       "       [[-6.68451932e+00]],\n",
+       "       [[-22.44949084]],\n",
        "\n",
-       "       [[-6.94188810e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-0576462e-1cfc-447f-8eda-0fd8ae68d9e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0576462e-1cfc-447f-8eda-0fd8ae68d9e3' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-a22001a4-adfc-48ae-9beb-5fc2e38e707d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a22001a4-adfc-48ae-9beb-5fc2e38e707d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2744ac-f7ec-4be7-adf9-ed41c2ad3cef' class='xr-var-data-in' type='checkbox'><label for='data-ec2744ac-f7ec-4be7-adf9-ed41c2ad3cef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[-21.68413141]]])</pre></div></div></li><li class='xr-section-item'><input id='section-fd450021-d8ba-42d2-aa3b-69eeb22279a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd450021-d8ba-42d2-aa3b-69eeb22279a7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31</div><input id='attrs-0d957ac3-3003-46b8-8588-748e2f7e9471' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d957ac3-3003-46b8-8588-748e2f7e9471' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-062d46dc-dbc0-456c-90f5-84f1ea5e4b11' class='xr-var-data-in' type='checkbox'><label for='data-062d46dc-dbc0-456c-90f5-84f1ea5e4b11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-03T00:00:00.000000000&#x27;, ..., &#x27;2022-12-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-12-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-47ca2d5c-3585-4d98-8dc4-4ceeb27d9342' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47ca2d5c-3585-4d98-8dc4-4ceeb27d9342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48aaf95c-61e2-431b-ada8-8863d715d134' class='xr-var-data-in' type='checkbox'><label for='data-48aaf95c-61e2-431b-ada8-8863d715d134' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8aec3fc3-a020-4826-8ab4-ddc944c1f6da' class='xr-section-summary-in' type='checkbox'  ><label for='section-8aec3fc3-a020-4826-8ab4-ddc944c1f6da' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b33f7d24-8d9d-46c5-ae94-3f290e76f4e5' class='xr-index-data-in' type='checkbox'/><label for='index-b33f7d24-8d9d-46c5-ae94-3f290e76f4e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-cff6627e-48dc-4421-a4b5-24f609ad6726' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cff6627e-48dc-4421-a4b5-24f609ad6726' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56693c41-dd79-4571-b605-7d4474ce6ed9' class='xr-var-data-in' type='checkbox'><label for='data-56693c41-dd79-4571-b605-7d4474ce6ed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11fccc1a-03b1-44b6-944e-a66fcd0315ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-11fccc1a-03b1-44b6-944e-a66fcd0315ba' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-655265f0-47ff-4040-afa2-8bb6b3f2140c' class='xr-index-data-in' type='checkbox'/><label for='index-655265f0-47ff-4040-afa2-8bb6b3f2140c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
        "               &#x27;2022-01-05&#x27;, &#x27;2022-01-06&#x27;, &#x27;2022-01-07&#x27;, &#x27;2022-01-08&#x27;,\n",
        "               &#x27;2022-01-09&#x27;, &#x27;2022-01-10&#x27;,\n",
        "               ...\n",
        "               &#x27;2022-12-22&#x27;, &#x27;2022-12-23&#x27;, &#x27;2022-12-24&#x27;, &#x27;2022-12-25&#x27;,\n",
        "               &#x27;2022-12-26&#x27;, &#x27;2022-12-27&#x27;, &#x27;2022-12-28&#x27;, &#x27;2022-12-29&#x27;,\n",
        "               &#x27;2022-12-30&#x27;, &#x27;2022-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-73e78ea4-7f75-4c43-8a61-95b14f1937d6' class='xr-index-data-in' type='checkbox'/><label for='index-73e78ea4-7f75-4c43-8a61-95b14f1937d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b247ac3d-a758-4b11-a6a4-a3dce5af9d65' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b247ac3d-a758-4b11-a6a4-a3dce5af9d65' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-053a980f-9e12-44ee-a6e2-5476bf6fda7e' class='xr-index-data-in' type='checkbox'/><label for='index-053a980f-9e12-44ee-a6e2-5476bf6fda7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2952476f-9ae8-423a-bcb9-8f52ade22d57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2952476f-9ae8-423a-bcb9-8f52ade22d57' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)>\n",
-       "array([[[ 1.09751547e+00]],\n",
+       "<TimeSeries (DataArray) (time: 365, component: 1, sample: 1)> Size: 3kB\n",
+       "array([[[  0.68180208]],\n",
        "\n",
-       "       [[ 1.12207918e+00]],\n",
+       "       [[ -0.66313743]],\n",
        "\n",
-       "       [[ 1.40083324e+00]],\n",
+       "       [[ -2.00833009]],\n",
        "\n",
-       "       [[ 9.47470373e-01]],\n",
+       "       [[ -2.61882592]],\n",
        "\n",
-       "       [[ 4.73560549e-01]],\n",
+       "       [[ -2.52592189]],\n",
        "\n",
-       "       [[ 9.04460441e-02]],\n",
+       "       [[ -3.03773304]],\n",
        "\n",
-       "       [[-7.44907944e-01]],\n",
+       "       [[ -5.12862143]],\n",
        "\n",
-       "       [[ 5.22522813e-01]],\n",
+       "       [[ -3.23933174]],\n",
        "\n",
-       "       [[-5.68699769e-01]],\n",
+       "       [[ -3.61242573]],\n",
        "\n",
-       "       [[-3.18083709e-01]],\n",
+       "       [[ -3.38090137]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[-5.26996197e+00]],\n",
+       "       [[-22.67596749]],\n",
        "\n",
-       "       [[-5.57221888e+00]],\n",
+       "       [[-22.40138059]],\n",
        "\n",
-       "       [[-5.85402550e+00]],\n",
+       "       [[-22.28243816]],\n",
        "\n",
-       "       [[-7.94180407e+00]],\n",
+       "       [[-22.55935878]],\n",
        "\n",
-       "       [[-7.52069468e+00]],\n",
+       "       [[-21.24834984]],\n",
        "\n",
-       "       [[-6.39405907e+00]],\n",
+       "       [[-21.00464561]],\n",
        "\n",
-       "       [[-7.71361505e+00]],\n",
+       "       [[-21.78252264]],\n",
        "\n",
-       "       [[-7.92228558e+00]],\n",
+       "       [[-22.49716263]],\n",
        "\n",
-       "       [[-6.68451932e+00]],\n",
+       "       [[-22.44949084]],\n",
        "\n",
-       "       [[-6.94188810e+00]]])\n",
+       "       [[-21.68413141]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2318,7 +2513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "id": "1ab88c7f-b09c-4ff3-9537-82d4c185c9f1",
    "metadata": {
     "ExecuteTime": {
@@ -2365,7 +2560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "id": "f5244eb1-4811-4c6d-baf3-33c52da1c824",
    "metadata": {
     "ExecuteTime": {
@@ -2412,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "id": "99fa151a-1fe4-4aea-95c0-8a6029e34c51",
    "metadata": {
     "ExecuteTime": {
@@ -2441,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "943287fb-a54d-4c3c-a797-ce44057241da",
    "metadata": {
     "ExecuteTime": {
@@ -2470,7 +2665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "id": "dea59b2d-2ce0-4916-a7a6-b6778bdbedfd",
    "metadata": {
     "ExecuteTime": {
@@ -2495,17 +2690,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-36dadb706e984443ad114ef79c50404f.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-36dadb706e984443ad114ef79c50404f.vega-embed details,\n",
+       "  #altair-viz-36dadb706e984443ad114ef79c50404f.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-36dadb706e984443ad114ef79c50404f\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-36dadb706e984443ad114ef79c50404f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-36dadb706e984443ad114ef79c50404f\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2014-12-31T23:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": null}, {\"time\": \"2015-01-01T00:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": null}, {\"time\": \"2015-01-01T01:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": 0.0}, {\"time\": \"2015-01-01T02:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": -0.3289758474798845}, {\"time\": \"2015-01-01T03:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": -0.08548257559864492}, {\"time\": \"2015-01-01T04:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": -0.2728733344583667}, {\"time\": \"2015-01-01T05:00:00.000\", \"variable\": \"generation biomass_generation nuclear\", \"value\": -0.34578130331572604}, 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\"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 25,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts_corr[0:100].plot();"
+    "ts_corr[0:100].plot()"
    ]
   },
   {
@@ -2532,7 +2797,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -2546,7 +2811,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
@@ -2563,4 +2828,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/docs/user_guide/0_core/0.7_custom-class.ipynb b/docs/user_guide/0_core/0.7_custom-class.ipynb
index 6ea8bde..0ede1b9 100644
--- a/docs/user_guide/0_core/0.7_custom-class.ipynb
+++ b/docs/user_guide/0_core/0.7_custom-class.ipynb
@@ -57,7 +57,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['../src', '/Users/fred.montet/src/ontime/docs/user_guide/0_core', '/Users/fred.montet/.pyenv/versions/3.10.10/lib/python310.zip', '/Users/fred.montet/.pyenv/versions/3.10.10/lib/python3.10', '/Users/fred.montet/.pyenv/versions/3.10.10/lib/python3.10/lib-dynload', '', '/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages', '/Users/fred.montet/src/ontime/src']\n"
+      "['../src', '/usr/lib/python310.zip', '/usr/lib/python3.10', '/usr/lib/python3.10/lib-dynload', '', '/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages', '/home/benjy/projects_dev/ontime/src']\n"
      ]
     }
    ],
@@ -79,16 +79,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -180,13 +171,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -231,7 +223,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -239,7 +231,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -251,6 +245,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -272,7 +270,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -283,7 +281,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -355,15 +353,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -513,50 +511,50 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt;\n",
-       "array([[[-0.36981256]],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt; Size: 40B\n",
+       "array([[[0.18681962]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[1.38205429]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[1.85408518]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[1.01011117]],\n",
        "\n",
-       "       [[-3.08642833]]])\n",
+       "       [[0.37180233]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object &#x27;random_walk&#x27;\n",
+       "  * time       (time) datetime64[ns] 40B 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 8B &#x27;random_walk&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-141b63a2-d126-4a03-8b03-0c269b01338c' class='xr-array-in' type='checkbox' checked><label for='section-141b63a2-d126-4a03-8b03-0c269b01338c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.3698 -1.896 -3.584 -4.196 -3.086</span></div><div class='xr-array-data'><pre>array([[[-0.36981256]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-989fb7be-4c76-4c6b-bb93-c859ac8f6b66' class='xr-array-in' type='checkbox' checked><label for='section-989fb7be-4c76-4c6b-bb93-c859ac8f6b66' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1868 1.382 1.854 1.01 0.3718</span></div><div class='xr-array-data'><pre>array([[[0.18681962]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[1.38205429]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[1.85408518]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[1.01011117]],\n",
        "\n",
-       "       [[-3.08642833]]])</pre></div></div></li><li class='xr-section-item'><input id='section-22d0f912-a183-42a5-95f1-0c93812dfbb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22d0f912-a183-42a5-95f1-0c93812dfbb1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-01-05</div><input id='attrs-e88e1cc7-dae0-4631-b684-2f1cd5c86409' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e88e1cc7-dae0-4631-b684-2f1cd5c86409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-666bc203-09d7-4717-9cfe-3c3a767c8da9' class='xr-var-data-in' type='checkbox'><label for='data-666bc203-09d7-4717-9cfe-3c3a767c8da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
+       "       [[0.37180233]]])</pre></div></div></li><li class='xr-section-item'><input id='section-df10056d-1882-46f3-9fb8-fe5e8cba86ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df10056d-1882-46f3-9fb8-fe5e8cba86ec' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-01-05</div><input id='attrs-92d0867f-399d-426d-afa2-093d0932f8a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92d0867f-399d-426d-afa2-093d0932f8a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c3b0ba4-aaed-4499-8c84-76ed9286b501' class='xr-var-data-in' type='checkbox'><label for='data-2c3b0ba4-aaed-4499-8c84-76ed9286b501' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2022-01-03T00:00:00.000000000&#x27;, &#x27;2022-01-04T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-0f76d333-e612-4e08-84e0-9703ec531342' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f76d333-e612-4e08-84e0-9703ec531342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2c5438-2055-4c73-b573-d81d4740099f' class='xr-var-data-in' type='checkbox'><label for='data-be2c5438-2055-4c73-b573-d81d4740099f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ac87cb3-5ab7-4e5b-90bb-a276b8fb471e' class='xr-section-summary-in' type='checkbox'  ><label for='section-4ac87cb3-5ab7-4e5b-90bb-a276b8fb471e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89bc0e8b-e62e-400e-8c1a-0a5fe0a23ff7' class='xr-index-data-in' type='checkbox'/><label for='index-89bc0e8b-e62e-400e-8c1a-0a5fe0a23ff7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
+       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;random_walk&#x27;</div><input id='attrs-653c8341-18ee-456d-b071-528930b133b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-653c8341-18ee-456d-b071-528930b133b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-122be2af-f666-421b-8562-a99f6997891a' class='xr-var-data-in' type='checkbox'><label for='data-122be2af-f666-421b-8562-a99f6997891a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;random_walk&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b99351cd-967c-4e33-87bf-87b1cdf3a8bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-b99351cd-967c-4e33-87bf-87b1cdf3a8bf' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bd7047f0-6981-4569-83cb-aa4bb64518a2' class='xr-index-data-in' type='checkbox'/><label for='index-bd7047f0-6981-4569-83cb-aa4bb64518a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
        "               &#x27;2022-01-05&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39a83d65-9269-4014-8f6f-a19a24f26edc' class='xr-index-data-in' type='checkbox'/><label for='index-39a83d65-9269-4014-8f6f-a19a24f26edc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a9c8868-1bc8-4ed9-a1f9-c182cca21185' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a9c8868-1bc8-4ed9-a1f9-c182cca21185' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea5c608c-bec7-40cd-8beb-294c04b63138' class='xr-index-data-in' type='checkbox'/><label for='index-ea5c608c-bec7-40cd-8beb-294c04b63138' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;random_walk&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0295b6c-81cb-4e84-bc75-e6318d35d630' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a0295b6c-81cb-4e84-bc75-e6318d35d630' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-0.36981256]],\n",
+       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)> Size: 40B\n",
+       "array([[[0.18681962]],\n",
        "\n",
-       "       [[-1.89552543]],\n",
+       "       [[1.38205429]],\n",
        "\n",
-       "       [[-3.58416814]],\n",
+       "       [[1.85408518]],\n",
        "\n",
-       "       [[-4.19590597]],\n",
+       "       [[1.01011117]],\n",
        "\n",
-       "       [[-3.08642833]]])\n",
+       "       [[0.37180233]]])\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object 'random_walk'\n",
+       "  * time       (time) datetime64[ns] 40B 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 8B 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
@@ -592,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f",
    "metadata": {
     "papermill": {
@@ -607,17 +605,87 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-5df078c6097d411a82a5e3500433cffc.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-5df078c6097d411a82a5e3500433cffc.vega-embed details,\n",
+       "  #altair-viz-5df078c6097d411a82a5e3500433cffc.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-5df078c6097d411a82a5e3500433cffc\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-5df078c6097d411a82a5e3500433cffc\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-5df078c6097d411a82a5e3500433cffc\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.18681961532209077}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.3820542853466793}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.854085177613871}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.010111169405978}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.3718023348558225}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.015417607485772311}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.11221215749090688}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.34378016179767235}, {\"time\": \"2022-01-09T00:00:00.000\", 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\"value\": -12.810353321235487}, {\"time\": \"2022-12-30T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -12.543721249792497}, {\"time\": \"2022-12-31T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -12.544337109911039}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": -26.990307035089664, \"max\": 1.854085177613871}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"random_walk\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"maxExtent\": 0, \"ticks\": false, \"gridScale\": \"y\", \"minExtent\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"orient\": \"bottom\", \"grid\": true, \"aria\": false, \"domain\": false, \"labels\": false}, {\"scale\": \"y\", \"minExtent\": 0, \"orient\": \"left\", \"gridScale\": \"x\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"grid\": true, \"domain\": false, \"maxExtent\": 0, \"zindex\": 0, \"aria\": false, \"ticks\": false, \"labels\": false}, {\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"orient\": \"bottom\", \"title\": \"time\", \"labelFlush\": true, \"zindex\": 0, \"grid\": false, \"labelOverlap\": true}, {\"scale\": \"y\", \"grid\": false, \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"orient\": \"left\", \"zindex\": 0, \"title\": \"value\"}], \"width\": 400, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"background\": \"white\", \"height\": 200, \"style\": \"cell\", \"padding\": 5}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "ts.plot();"
+    "ts.plot()"
    ]
   },
   {
@@ -657,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "40a0dd53-0fe9-40a7-86d9-ee551e4c5e6e",
    "metadata": {
     "papermill": {
@@ -702,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "6a8dd074-6350-4c3a-a8a7-7d901b790f95",
    "metadata": {
     "papermill": {
@@ -718,10 +786,10 @@
     {
      "data": {
       "text/plain": [
-       "['threshold', 'quantile', 'my_detector']"
+       "['threshold', 'quantile']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -732,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "91bdf719-f451-4d07-aaed-c2f5f7b4fa1b",
    "metadata": {
     "papermill": {
@@ -751,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "bb636aa5-f155-46e4-8038-027d5f5db78a",
    "metadata": {
     "papermill": {
@@ -767,10 +835,10 @@
     {
      "data": {
       "text/plain": [
-       "['threshold', 'quantile', 'my_detector', 'my_ae']"
+       "['threshold', 'quantile', 'my_ae']"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "f16ac090-142d-4d1b-8351-40b443275c72",
    "metadata": {
     "papermill": {
@@ -830,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "id": "fe6b4821-9365-4356-b53d-cd5f1fb222ef",
    "metadata": {},
    "outputs": [
@@ -874,13 +942,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -925,7 +994,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -933,7 +1002,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -945,6 +1016,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -966,7 +1041,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -977,7 +1052,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1049,15 +1124,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1207,7 +1282,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)&gt; Size: 40B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
@@ -1218,12 +1293,12 @@
        "\n",
        "       [[121.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01\n",
-       "  * component  (component) object &#x27;#Passengers&#x27;\n",
+       "  * Month      (Month) datetime64[ns] 40B 1949-01-01 1949-02-01 ... 1949-05-01\n",
+       "  * component  (component) object 8B &#x27;#Passengers&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-75a93770-ad98-48f7-8894-e8001ee86587' class='xr-array-in' type='checkbox' checked><label for='section-75a93770-ad98-48f7-8894-e8001ee86587' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0 129.0 121.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-899e7b1e-cff4-4584-801d-e2b13b0d5e02' class='xr-array-in' type='checkbox' checked><label for='section-899e7b1e-cff4-4584-801d-e2b13b0d5e02' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0 129.0 121.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
@@ -1231,14 +1306,14 @@
        "\n",
        "       [[129.]],\n",
        "\n",
-       "       [[121.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-849507c4-c8cb-4884-a869-1b47c9b94e6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-849507c4-c8cb-4884-a869-1b47c9b94e6f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 ... 1949-05-01</div><input id='attrs-a6affe79-2eed-42e4-a874-d87a5ce38d9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6affe79-2eed-42e4-a874-d87a5ce38d9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3caf73da-cb3d-4d17-8d6b-4350a775e4b3' class='xr-var-data-in' type='checkbox'><label for='data-3caf73da-cb3d-4d17-8d6b-4350a775e4b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
+       "       [[121.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-1d4ec9d9-c580-4d15-8a47-a06d93418722' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d4ec9d9-c580-4d15-8a47-a06d93418722' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 ... 1949-05-01</div><input id='attrs-62959d9e-de22-4384-b67e-1e11188219dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62959d9e-de22-4384-b67e-1e11188219dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b4ec6f-176d-485f-adce-b63d9fb45bde' class='xr-var-data-in' type='checkbox'><label for='data-e6b4ec6f-176d-485f-adce-b63d9fb45bde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1949-03-01T00:00:00.000000000&#x27;, &#x27;1949-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1949-05-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-6ad2aa39-c408-4d31-91d4-2f06180ab432' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ad2aa39-c408-4d31-91d4-2f06180ab432' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-125d80bf-7c2c-4641-8b60-f69e7f5b083d' class='xr-var-data-in' type='checkbox'><label for='data-125d80bf-7c2c-4641-8b60-f69e7f5b083d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa50035d-df2e-4977-95e1-2cf5cbf55c61' class='xr-section-summary-in' type='checkbox'  ><label for='section-fa50035d-df2e-4977-95e1-2cf5cbf55c61' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08461527-594f-4631-81ae-d78ae41f6f88' class='xr-index-data-in' type='checkbox'/><label for='index-08461527-594f-4631-81ae-d78ae41f6f88' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;, &#x27;1949-04-01&#x27;,\n",
+       "       &#x27;1949-05-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-430be8a3-10d4-4250-880d-45576dff296f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-430be8a3-10d4-4250-880d-45576dff296f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9a3a20-16e4-429e-9042-22e99b25051e' class='xr-var-data-in' type='checkbox'><label for='data-9b9a3a20-16e4-429e-9042-22e99b25051e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eda98ad7-0a23-4996-a5c7-28b5390b57f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-eda98ad7-0a23-4996-a5c7-28b5390b57f1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-55ef86aa-c3da-454a-8709-3c68dd3d1fe3' class='xr-index-data-in' type='checkbox'/><label for='index-55ef86aa-c3da-454a-8709-3c68dd3d1fe3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;, &#x27;1949-04-01&#x27;,\n",
        "               &#x27;1949-05-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1459d1c6-dfc5-4c8e-b958-3b29c3c90934' class='xr-index-data-in' type='checkbox'/><label for='index-1459d1c6-dfc5-4c8e-b958-3b29c3c90934' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6b2f247-d3a3-4f03-ab4c-2359f5b7bf90' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6b2f247-d3a3-4f03-ab4c-2359f5b7bf90' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b691bb85-4e29-49b1-b5ce-865882eab9a7' class='xr-index-data-in' type='checkbox'/><label for='index-b691bb85-4e29-49b1-b5ce-865882eab9a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d6c3cec-413e-48d0-b316-5925fcc77aad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d6c3cec-413e-48d0-b316-5925fcc77aad' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)> Size: 40B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
@@ -1249,15 +1324,15 @@
        "\n",
        "       [[121.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01\n",
-       "  * component  (component) object '#Passengers'\n",
+       "  * Month      (Month) datetime64[ns] 40B 1949-01-01 1949-02-01 ... 1949-05-01\n",
+       "  * component  (component) object 8B '#Passengers'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,7 +1347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "id": "dd88e23a",
    "metadata": {},
    "outputs": [
@@ -1316,13 +1391,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1367,7 +1443,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1375,7 +1451,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1387,6 +1465,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1408,7 +1490,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1419,7 +1501,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1491,15 +1573,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1649,7 +1731,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)&gt; Size: 40B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
@@ -1660,12 +1742,12 @@
        "\n",
        "       [[121.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01\n",
-       "  * component  (component) object &#x27;#Passengers&#x27;\n",
+       "  * Month      (Month) datetime64[ns] 40B 1949-01-01 1949-02-01 ... 1949-05-01\n",
+       "  * component  (component) object 8B &#x27;#Passengers&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-040a97c8-8994-455a-9134-7dd471fd6f9d' class='xr-array-in' type='checkbox' checked><label for='section-040a97c8-8994-455a-9134-7dd471fd6f9d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0 129.0 121.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6ebc44c5-3b1d-40fb-99f4-8ac5be76e6ec' class='xr-array-in' type='checkbox' checked><label for='section-6ebc44c5-3b1d-40fb-99f4-8ac5be76e6ec' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0 129.0 121.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
        "\n",
@@ -1673,14 +1755,14 @@
        "\n",
        "       [[129.]],\n",
        "\n",
-       "       [[121.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-96dd2866-173f-46c9-b097-02751e59d1e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96dd2866-173f-46c9-b097-02751e59d1e2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 ... 1949-05-01</div><input id='attrs-284ca365-86e2-424a-905a-4a57ecd4e4b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-284ca365-86e2-424a-905a-4a57ecd4e4b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7670dba4-1d2b-4351-8823-d7fed0b81403' class='xr-var-data-in' type='checkbox'><label for='data-7670dba4-1d2b-4351-8823-d7fed0b81403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
+       "       [[121.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-f4966e60-b44d-4bfb-a21d-5f86db0481ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4966e60-b44d-4bfb-a21d-5f86db0481ed' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 ... 1949-05-01</div><input id='attrs-e9829cf0-ba7a-48d8-b225-6b733fc69273' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9829cf0-ba7a-48d8-b225-6b733fc69273' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9244585-932e-4cda-bbb8-7d62ec228fc8' class='xr-var-data-in' type='checkbox'><label for='data-f9244585-932e-4cda-bbb8-7d62ec228fc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;1949-03-01T00:00:00.000000000&#x27;, &#x27;1949-04-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1949-05-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-230a3a1c-1229-44a4-87ee-4969a718c71f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-230a3a1c-1229-44a4-87ee-4969a718c71f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d535190-d046-4571-813f-adb9b6377b07' class='xr-var-data-in' type='checkbox'><label for='data-4d535190-d046-4571-813f-adb9b6377b07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed599bfa-812e-45f9-a73c-79d74ac959cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-ed599bfa-812e-45f9-a73c-79d74ac959cc' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1394cc6-8d2b-402f-9bef-d94a0d038697' class='xr-index-data-in' type='checkbox'/><label for='index-b1394cc6-8d2b-402f-9bef-d94a0d038697' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;, &#x27;1949-04-01&#x27;,\n",
+       "       &#x27;1949-05-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-96110bbd-1b83-4ed9-86dc-f791ad25cb18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96110bbd-1b83-4ed9-86dc-f791ad25cb18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-318402b6-5542-495d-a8cc-ffac732b59c5' class='xr-var-data-in' type='checkbox'><label for='data-318402b6-5542-495d-a8cc-ffac732b59c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-940af433-c91b-4e9c-a6f1-6477f21c1431' class='xr-section-summary-in' type='checkbox'  ><label for='section-940af433-c91b-4e9c-a6f1-6477f21c1431' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-25d39a74-273e-4138-8574-9f7b5a13d16c' class='xr-index-data-in' type='checkbox'/><label for='index-25d39a74-273e-4138-8574-9f7b5a13d16c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;, &#x27;1949-03-01&#x27;, &#x27;1949-04-01&#x27;,\n",
        "               &#x27;1949-05-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-627475dc-92a1-4615-b097-7f5e86d1853f' class='xr-index-data-in' type='checkbox'/><label for='index-627475dc-92a1-4615-b097-7f5e86d1853f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c56adfbc-af46-470d-96e3-d643c546140b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c56adfbc-af46-470d-96e3-d643c546140b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Month&#x27;, freq=&#x27;MS&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5cacc3dd-be12-4643-88c1-971b8445e3a9' class='xr-index-data-in' type='checkbox'/><label for='index-5cacc3dd-be12-4643-88c1-971b8445e3a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;#Passengers&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7891bffd-3c53-4751-b816-c5bfe87d0d64' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7891bffd-3c53-4751-b816-c5bfe87d0d64' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)>\n",
+       "<TimeSeries (DataArray) (Month: 5, component: 1, sample: 1)> Size: 40B\n",
        "array([[[112.]],\n",
        "\n",
        "       [[118.]],\n",
@@ -1691,15 +1773,15 @@
        "\n",
        "       [[121.]]])\n",
        "Coordinates:\n",
-       "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1949-05-01\n",
-       "  * component  (component) object '#Passengers'\n",
+       "  * Month      (Month) datetime64[ns] 40B 1949-01-01 1949-02-01 ... 1949-05-01\n",
+       "  * component  (component) object 8B '#Passengers'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1722,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "id": "3de25cf0-c333-48e1-b34b-c82724fc5ec9",
    "metadata": {},
    "outputs": [
@@ -1762,7 +1844,7 @@
        "      <th>Volume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Date</th>\n",
+       "      <th>time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -1823,7 +1905,7 @@
       ],
       "text/plain": [
        "            Open      High       Low     Close  Adj_Close  Volume\n",
-       "Date                                                             \n",
+       "time                                                             \n",
        "1980-03-17   0.0  3.302083  3.125000  3.145833   3.145833  219600\n",
        "1980-03-18   0.0  3.125000  2.937500  3.031250   3.031250  727200\n",
        "1980-03-19   0.0  3.083333  3.020833  3.041667   3.041667  295200\n",
@@ -1831,7 +1913,7 @@
        "1980-03-21   0.0  3.020833  2.906250  2.916667   2.916667  130800"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1844,12 +1926,13 @@
     "# set index to be compliant with TimeEval's canonical format\n",
     "pandasAmd.index= pandasAmd['Date'].astype('datetime64[ns]')\n",
     "del pandasAmd['Date']\n",
+    "pandasAmd = pandasAmd.rename_axis(\"time\")\n",
     "pandasAmd.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "id": "ba52b0d5-804e-4839-a2a6-c4060e8c5f72",
    "metadata": {},
    "outputs": [
@@ -1886,13 +1969,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1937,7 +2021,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1945,7 +2029,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1957,6 +2043,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1978,7 +2068,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1989,7 +2079,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -2061,15 +2151,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -2219,7 +2309,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Date: 5, component: 6, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 6, sample: 1)&gt; Size: 240B\n",
        "array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
@@ -2255,12 +2345,12 @@
        "        [2.91666698e+00],\n",
        "        [1.30800000e+05]]])\n",
        "Coordinates:\n",
-       "  * Date       (Date) datetime64[ns] 1980-03-17 1980-03-18 ... 1980-03-21\n",
-       "  * component  (component) object &#x27;Open&#x27; &#x27;High&#x27; &#x27;Low&#x27; ... &#x27;Adj_Close&#x27; &#x27;Volume&#x27;\n",
+       "  * time       (time) datetime64[ns] 40B 1980-03-17 1980-03-18 ... 1980-03-21\n",
+       "  * component  (component) object 48B &#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Adj_Close&#x27; &#x27;Volume&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Date</span>: 5</li><li><span class='xr-has-index'>component</span>: 6</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3f28434f-c598-49c7-ae40-ad565f57d2cb' class='xr-array-in' type='checkbox' checked><label for='section-3f28434f-c598-49c7-ae40-ad565f57d2cb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 3.302 3.125 3.146 3.146 ... 3.021 2.906 2.917 2.917 1.308e+05</span></div><div class='xr-array-data'><pre>array([[[0.00000000e+00],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 6</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-84196bec-0045-4c0d-a5c9-6f732dea7821' class='xr-array-in' type='checkbox' checked><label for='section-84196bec-0045-4c0d-a5c9-6f732dea7821' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 3.302 3.125 3.146 3.146 ... 3.021 2.906 2.917 2.917 1.308e+05</span></div><div class='xr-array-data'><pre>array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
        "        [3.14583302e+00],\n",
@@ -2293,14 +2383,14 @@
        "        [2.90625000e+00],\n",
        "        [2.91666698e+00],\n",
        "        [2.91666698e+00],\n",
-       "        [1.30800000e+05]]])</pre></div></div></li><li class='xr-section-item'><input id='section-7ceeefb2-52bc-47c8-9e84-e48b01021d32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ceeefb2-52bc-47c8-9e84-e48b01021d32' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Date</span></div><div class='xr-var-dims'>(Date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-03-17 ... 1980-03-21</div><input id='attrs-d9e4a12f-5b2a-4e6a-bcd7-e0e90604845b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9e4a12f-5b2a-4e6a-bcd7-e0e90604845b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7141fff0-aaf6-4e5b-afad-a23f539a3646' class='xr-var-data-in' type='checkbox'><label for='data-7141fff0-aaf6-4e5b-afad-a23f539a3646' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-03-17T00:00:00.000000000&#x27;, &#x27;1980-03-18T00:00:00.000000000&#x27;,\n",
+       "        [1.30800000e+05]]])</pre></div></div></li><li class='xr-section-item'><input id='section-9d4519a4-154b-4ca3-a97f-615159394bb4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d4519a4-154b-4ca3-a97f-615159394bb4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-03-17 ... 1980-03-21</div><input id='attrs-acf4416e-7544-427c-807c-b833d37b5122' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acf4416e-7544-427c-807c-b833d37b5122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7911274f-0744-4350-898f-f8154887b812' class='xr-var-data-in' type='checkbox'><label for='data-7911274f-0744-4350-898f-f8154887b812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-03-17T00:00:00.000000000&#x27;, &#x27;1980-03-18T00:00:00.000000000&#x27;,\n",
        "       &#x27;1980-03-19T00:00:00.000000000&#x27;, &#x27;1980-03-20T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1980-03-21T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Volume&#x27;</div><input id='attrs-47720fe0-19fa-41ea-9b92-94fcad365d73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47720fe0-19fa-41ea-9b92-94fcad365d73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24977043-fa24-4528-af16-35c0cc29f1a4' class='xr-var-data-in' type='checkbox'><label for='data-24977043-fa24-4528-af16-35c0cc29f1a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbe92aa1-a101-4f25-a0a6-2e4fe51ad4d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-dbe92aa1-a101-4f25-a0a6-2e4fe51ad4d6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c47067c5-485b-4802-b04a-4bfa43dae6a6' class='xr-index-data-in' type='checkbox'/><label for='index-c47067c5-485b-4802-b04a-4bfa43dae6a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-03-17&#x27;, &#x27;1980-03-18&#x27;, &#x27;1980-03-19&#x27;, &#x27;1980-03-20&#x27;,\n",
+       "       &#x27;1980-03-21T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Open&#x27; &#x27;High&#x27; ... &#x27;Volume&#x27;</div><input id='attrs-cf1d63e8-2753-4fc1-80d6-96444d490202' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf1d63e8-2753-4fc1-80d6-96444d490202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f7577e-f0c9-4141-86da-7a34b6516eb5' class='xr-var-data-in' type='checkbox'><label for='data-33f7577e-f0c9-4141-86da-7a34b6516eb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e12f3a26-8ee6-4e2f-882a-5f4abb3b5463' class='xr-section-summary-in' type='checkbox'  ><label for='section-e12f3a26-8ee6-4e2f-882a-5f4abb3b5463' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-66a499a3-598c-4d0d-b170-508cce50845b' class='xr-index-data-in' type='checkbox'/><label for='index-66a499a3-598c-4d0d-b170-508cce50845b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-03-17&#x27;, &#x27;1980-03-18&#x27;, &#x27;1980-03-19&#x27;, &#x27;1980-03-20&#x27;,\n",
        "               &#x27;1980-03-21&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Date&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0eb5e55-97c2-4063-89b5-190c89d8d387' class='xr-index-data-in' type='checkbox'/><label for='index-d0eb5e55-97c2-4063-89b5-190c89d8d387' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f926bb9c-22dd-4c67-b319-edfecb07814c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f926bb9c-22dd-4c67-b319-edfecb07814c' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-79b8259e-7260-464f-add2-e7dc762b8f87' class='xr-index-data-in' type='checkbox'/><label for='index-79b8259e-7260-464f-add2-e7dc762b8f87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Open&#x27;, &#x27;High&#x27;, &#x27;Low&#x27;, &#x27;Close&#x27;, &#x27;Adj_Close&#x27;, &#x27;Volume&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4fc985d-afe4-4b4c-aff6-0ac6f90f4c5a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4fc985d-afe4-4b4c-aff6-0ac6f90f4c5a' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (Date: 5, component: 6, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 5, component: 6, sample: 1)> Size: 240B\n",
        "array([[[0.00000000e+00],\n",
        "        [3.30208302e+00],\n",
        "        [3.12500000e+00],\n",
@@ -2336,15 +2426,15 @@
        "        [2.91666698e+00],\n",
        "        [1.30800000e+05]]])\n",
        "Coordinates:\n",
-       "  * Date       (Date) datetime64[ns] 1980-03-17 1980-03-18 ... 1980-03-21\n",
-       "  * component  (component) object 'Open' 'High' 'Low' ... 'Adj_Close' 'Volume'\n",
+       "  * time       (time) datetime64[ns] 40B 1980-03-17 1980-03-18 ... 1980-03-21\n",
+       "  * component  (component) object 48B 'Open' 'High' ... 'Adj_Close' 'Volume'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2356,29 +2446,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "id": "fdc114d4-e8a0-4093-9556-c02a4ca81a18",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-171ffd706a5a413ba26404e88fa180a0.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-171ffd706a5a413ba26404e88fa180a0.vega-embed details,\n",
+       "  #altair-viz-171ffd706a5a413ba26404e88fa180a0.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-171ffd706a5a413ba26404e88fa180a0\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-171ffd706a5a413ba26404e88fa180a0\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-171ffd706a5a413ba26404e88fa180a0\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"1980-03-17T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-18T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-19T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-20T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-21T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-22T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-03-23T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-03-24T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-25T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-26T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-27T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-28T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-03-29T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-03-30T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-03-31T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-01T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-02T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-03T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-04T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-05T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-06T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-07T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-08T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-09T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-10T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-11T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-12T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-13T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-14T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-15T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-16T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-17T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-18T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-19T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-20T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-21T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-22T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-23T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-24T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-25T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-26T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-27T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-04-28T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-29T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-04-30T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-01T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-02T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-03T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-04T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-05T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-06T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-07T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-08T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-09T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-10T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-11T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-12T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-13T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-14T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-15T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-16T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-17T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-18T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-19T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-20T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-21T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-22T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-23T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-24T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-25T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-26T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-05-27T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-28T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-29T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-30T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-05-31T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-01T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-02T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-03T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-04T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-05T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-06T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-07T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-08T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-09T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-10T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-11T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-12T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-13T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-14T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-15T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-16T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-17T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-18T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-19T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-20T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-21T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-22T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-23T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-24T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-25T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-26T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-27T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-06-28T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-29T00:00:00.000\", \"variable\": \"Open\", \"value\": null}, {\"time\": \"1980-06-30T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": \"1980-07-01T00:00:00.000\", \"variable\": \"Open\", \"value\": 0.0}, {\"time\": 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\"value\": 34263800.0}, {\"time\": \"2021-04-15T00:00:00.000\", \"variable\": \"Volume\", \"value\": 68942800.0}, {\"time\": \"2021-04-16T00:00:00.000\", \"variable\": \"Volume\", \"value\": 47246000.0}, {\"time\": \"2021-04-17T00:00:00.000\", \"variable\": \"Volume\", \"value\": null}, {\"time\": \"2021-04-18T00:00:00.000\", \"variable\": \"Volume\", \"value\": null}, {\"time\": \"2021-04-19T00:00:00.000\", \"variable\": \"Volume\", \"value\": 39070100.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": 0.0, \"max\": 325058400.0}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"Open\"}, {\"variable\": \"High\"}, {\"variable\": \"Low\"}, {\"variable\": \"Close\"}, {\"variable\": \"Adj_Close\"}, {\"variable\": \"Volume\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"domain\": false, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"orient\": \"bottom\", \"grid\": true, \"labels\": false, \"aria\": false, \"gridScale\": \"y\", \"maxExtent\": 0, \"minExtent\": 0, \"ticks\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"ticks\": false, \"grid\": true, \"aria\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"maxExtent\": 0, \"zindex\": 0, \"minExtent\": 0, \"gridScale\": \"x\", \"labels\": false, \"domain\": false}, {\"scale\": \"x\", \"zindex\": 0, \"title\": 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+       "</script>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='Date'>"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2397,29 +2547,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "id": "1217800d-503b-4939-adc0-3f7cd96c7a49",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-a1c8516ed1e742a7b8e84919f80288cb.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-a1c8516ed1e742a7b8e84919f80288cb.vega-embed details,\n",
+       "  #altair-viz-a1c8516ed1e742a7b8e84919f80288cb.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-a1c8516ed1e742a7b8e84919f80288cb\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-a1c8516ed1e742a7b8e84919f80288cb\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-a1c8516ed1e742a7b8e84919f80288cb\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.6201824089920783}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.1144715603292222}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.7570593947007056}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.1689280356899874}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 1.107067901638575}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -0.6521258468793709}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -2.176411922990987}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": -3.52588428917208}, {\"time\": \"2022-01-09T00:00:00.000\", 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+       "</script>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='time'>"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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TKPtVuEMwAYCnn37a4eddunQpjh49Kr22tBlHpNPpMG7cOAwePBi9evUCAJSUlODnn38GAJw5cwZLly7V/HxVJZ/1ODMz0+7HJyIi12AwgfJJFFcEE63mj4qKClRUVDj0vGKlRDRu3Dibj9W7d29peeHChbhy5QpSUlIwbdo0vPjiizYf1xh5HxdnVGiIiMg5GEwAJCQkICwsDICyI6gzlJeXG32SRRxbxJ5u374tzSKcn58vrT948CA6duxo83HlweT7779HfHw80tPTAQArVqyw+bhyJ06cwDfffIPy8nL4+vpK69nHhIjIezCYAPDx8ZGaMVJTU5GVleW0c+fm5hrdVlhYaNdzZWRkIDExEY0bN8a2bdsUwSQhIaFKx46JiUGbNm2qeolG3bhxA/feey+GDx+ODz74QFFlUj+Zc+LECezdu9dh10JERI7DYPI/8n4mzqyamOp/Ye+KybZt25CbmwtBEHD//fcrgolYMaqKv//971U+hjEffPCBFNSeffZZxbasrCzpPh44cABJSUlISUnB119/7bDrISIix2Aw+R95x8/ffvvNaeeV/+Y/ffp0TJkyRXpt72CiHmPk8OHDACorRkFBQVU+/tixY3HgwIEqH0ettLQUJ0+eNLnPhQsXoNfr8dRTT6GsrAxAZfMUERF5FtdO0uJGmjVrJi1fu3bNaeeVV0wiIiIUYcTeweTmzZuK1+Lxw8LCrHpM2BTxsWF7WbduHR588EHo9XqT+50/fx7Hjx9XPGWk7txLRETuj8HkfyIiIqRlZ85dow4mco4OJqIaNWrY7RwxMTEICgpCcXGxYn1paSkCAgKsPt4///lPs6EEAPbs2YP169cr1jGYEBF5Hjbl/I+rgon8XOHh4QgNDZVeOyuY2KN/iUin0yE+Pt5gvS2fJSsrC3/++adF+3700UeKsU0ABhMiIk/EYPI/4eHh0rIrKyaeHkwAaAYTeUdbS/34449m9xkyZIjidVBQkPR5xMeViYjIczCY/E9ISAj8/CpbthwxUqkx8kddIyMjvSKYNGzY0GCdLcFk586d0nJcXJzB9pCQECxcuFD6cwsICMAXX3yBxo0bA6ismLhiUkYiIrIdg8n/6HQ61KxZE4DzKiYlJSVSv4iQkBAkJSU5LJgIguC0YBISEmKwrioVEz8/P5w+fRoTJkxQbG/YsCEaN26M1atXY8yYMfjhhx8wcuRIREdHA6i8v8YGryMiIvfEzq8yNWvWRFZWFnJyclBWVqYY9twRtmzZIlVnhg8fjrCwMIcFk+zsbOkxWjV7BxOxgiFnSzC5evUqAKBp06YICwtD7dq1FdvFysyYMWMwZswYaX1MTIy0nJ6eLgVOIiJyf6yYyIhfYNnZ2QgLC8OMGTNM7v/LL79g4sSJ2Ldvn03n+/LLL6Xl8ePHA1BWG+wZTG7cuGF0m72DybRp0+Djo/zRsjaYlJeXS59f7P8TFRWl2EeryQiAVDEB2AGWiMjTMJjIyH+zLi0txeLFi002BaSkpODTTz9F9+7dbTqf2L/Ex8cHffr0AQCHVUyMNeMA9g8mTZs2xdatWxWD1lkbTOSDwdWqVQsAjFZM1BhMiIg8F4OJjFbJ/8yZM0b3LykpkZZtmQlY/PKtUaOG1PzhjGCiDiL2DiYA0LdvXzz11FPSa3sEE1sqJnwyh4jIszCYyGgFE2PjaJSWlipe2/KbuViNkZ9XHkzsOYmfPJi0aNFCsc2eA6zJyQOPtcFEPrmheH8srZjI+5iwYkJE5FkYTGSsCSaZmZmK19evX7f6fGIwkQcDR1VM5IOPyYffBxxTMVEftyrBpCoVEwYTIiLPwmAi48xgUlFRIQUPZwQTedOIej4bTwkm6opJ/fr1Nd/LYEJE5LkYTGSsCSa3b99WvD5z5oxBWDFF/kUtP6+jnsqRBxN1pcEdg4m5PiaxsbEIDAzUfK+8KcfU00hEROR+GExktILJ+fPnDfqTAIbB5IUXXkBiYiKOHDli0bnkT/vIKyY+Pj4IDg4GYN9gIq9AqIeMd3YwEQQBp0+fNtlhWKuPSUBAAJo2bQoA6Nixo9H3BgcHS3Mf2dLERkRErsNgIiP+Zi5XUVGhGDZepFUdycnJQc+ePS16QsdYMAH+as4Rg8m1a9fw1ltv4ezZs2aPa4y8AuHqYPLss8+iZcuWGDlypNH3ajXlAMDatWsxe/ZsfPjhhybPXa9ePQCVwYTD0hMReQ4GExljI4Rq/datrpiI8vPz8fnnn5s9lzwoGAsmFy9eRKdOnVC/fn28/PLLGDdunNnjmjtfQECAoqkDcH4wef/99wEA3377rdHRaLWacgCgXbt2eP311w36yaiJwaS4uNipcx8REVHVMJjIGAsm8t/eRcaCCQBs3brV7LnkFRP1eeUdYOVNQ0ePHjX6RW6O+EVfs2ZNg/M5KpjIP4cYTNR9TcRh5+UEQTBaMbGUfNI/NucQEXkOBhMZa4KJqY6ulnS4tKQpx9ZjaxE/Q82aNQ3msnFUMPHz80NQUBCAvwKJulns8uXLAIBdu3ahR48eaN68OUJCQrBkyRJpH1vmuhErJkBlUxgREXkGBhMZe1VMLPkN3dZgIlYYBEHApUuXLOo/IQiCVDHRqj44aoA14K/QIwaTc+fOKbanpqYCAGbOnIk9e/bg7NmzKC4uVuzDigkRUfXBYCITGBiI1157DfHx8XjyySel9fJgkp2djfLycrPBxFxgkPehUAeigIAAaTkiIgJvvfWW9FoMJpMnT0ZCQgJmzpxp5lNVDp0vNgFphS/xKSBHMBdMLl++jJKSEpMde20JJqyYEBF5JgYTldmzZ+Py5ct4+OGHpXW5ubkoLy/Hyy+/jDp16qBt27YGX3Z169ZF586dAVQOJS8PHlpMVUzE5g0AaNWqlWIgsatXr0IQBHzyyScAgMWLFwMAlixZgilTpmhO1qf16O3ChQvh4+ODyZMnQ6fTmbzWqhCDifh5tSomly9fhl6vBwD07NlTsT0oKEgR1CzFigkRkWdiMDFC/lt6bm4uZs2ahbfeegsVFRU4ffq09GXXrFkz7N+/H7/++qtiqHdzX4amgom8etCyZUs0aNBAep2WlmbQz+TXX3/F9OnTsWLFCvzzn/80OJfWEy6zZs1Cbm4uli9fbvI6q0ochbWoqAh79+7VrJhcuHBBet2lSxfFvbelfwnAigkRkadiMDFCHUy2b9+uuV+dOnVw3333ITY2VvFlaE0wUX/5jhgxQloeMGCAQcVE3YF09erV0rJW0DDWbOSoTq9yEydOlJbffPNNzYqJ/PMkJiaiefPm0mtbqiVA5eivPj6VP96smBAReQ4GEyPkwSQvL08xCZ6cfJh0a4KJqXFM5s2bh3vvvRdTp07FsGHDFMfVCiYnTpwweS6tphxneeihh9CkSRMAwM6dOw3mrklLS1NUiJo0aaKoPNk6142fn580XgsrJkREnoPBxIiwsDCp78Xt27c1n8wBgLvuuktatrViog4mzZo1w88//4yPPvoIOp0OgYGB0pdsWlqaoukDAI4dO2b0WIDpjraO5ufnh8cee8zo9vLycuzdu1d6nZiYqGi60poOwFKxsbEAKsMNR38lIvIMDCZG+Pj4SF/iFy9eNLrf/fffLy3bK5hoEZtzbty4gdOnTyu2yZ8QioyMNHivsVFUnaVly5Ymt//+++8AKicwjI2NlQJFVYWHhwOonFagsLDQLsckIiLHYjAxQfwSN/WEjfgkDqAMJuYGQpMf05K+HmIw0ev12LNnj9H91COrrlixAo8++qj02tkVEwCKphmRVhWlSZMm0Ol0invatWtXm8+r7idERETuj8HEBHPVhYCAAMUoqnXr1pWWLa2YhIWFSZ00TWnVqpW0bGrU2ezsbOnR2+LiYkyZMkWx3RXBRAwccpMnTzYYgVacObhz58546aWX0LVrVyxdutTm8zKYEBF5HgYTE7SCyYABA6TllStXKrYFBgZKTSnyisnt27fx7bffKpoTxGBi6airpmbildPr9dKxT506ZbDdFU05QUFBaNiwoWJdu3btkJycrFj30EMPScvz58/Hvn37FIHMWgwmRESeh8HEBK0v8VGjRuGzzz7D0qVLFV+k6vfIm2oGDx6MYcOGYdq0adI6MTxYWsHo2LGj2Rl1RVlZWQCA48ePG2xzRcUE+KsaAlQGlZCQEKSkpEjratWqZXH4shSDCRGR52EwMUErmNSpUwcTJkzAE088oTliqvjFLwaPO3fu4ODBgwCAzz//HICyqmFpxUSn02Hs2LHSa7Fjp5bs7GwAyqd11NfnbLVr1zZYN2XKFNSqVQthYWHYvHmz3Ueg9dZgUlZWhtGjR6Nnz542P05NROSunB5MkpKS0LVrV3Tr1g3dunWThlZ3R1rBROsLVk784i8pKUFJSYnmEz0ZGRlSP5A6depYfD0zZsxAXFwc4uLi8OOPPxoNNWIwcaeKiTx0iI/uNm7cWBr5tUuXLnY/p7cGk08++QTr1q3D7t278dJLL7n6coiI7MrP/C72t379emlcDndmSzCRh4U7d+4YjDmi1+uRlpYmvZaP2WFO/fr1kZaWhvLycvj7+yseOZbLysqCIAiawcQVfUyAyurImjVrAABvv/22tN5U5aeqvDWY7Nu3T1peu3atQV8nIiJP5pJgYqnS0lKDAbb8/PxsHqbcFLGCIf4f0K4uREZGKvZRkweT3Nxcg1Fac3JykJqaKr2uX7++yeNp8fX1NXhPhw4d8NtvvwGofGrn0qVL0pdxREQEWrdujbFjx8LPz8/q84m07pGlunfvjkWLFiEjIwNTpkyx+RqsIf+zyMnJcco55apyv0xRDxbn7M/lCI66V96M98xyvFfWcdT9suQJVMBFweTRRx+Vxqt45plnjP7WvHLlSoO5X0aPHo0xY8Y47Nrk1Yzy8nLFNl9fX+Tk5Jj87VveZHHmzBmDfh6///67NKAYAAQHByuCijVGjx6NtWvXAqgc6E0MJpcuXZL6tYj7vfzyywBg87nk5PfIGoMGDQIAzRmQHaGoqEhavnr1ql0+uy1svV/GyH/+BEFw2edyBHvfq+qA98xyvFfWsff9aty4sUX7OT2YLF++HG3btsWdO3fw1ltvYc6cOViwYIHmvpMmTcLDDz+sWOfIiklaWhoaNGggpTr1Taxdu7bZJ2Pkg6yFhoYadE4MCQlBQUGB9Lp9+/aIj4+36Zrnz5+P0NBQpKSkoGHDhpg3bx6AyqaSpKQkab/GjRvbfA45rXvkzuTBRK/X2+UeWMNR90sdjJ39uRzB03623AHvmeV4r6zj6vtl12Dy+OOPa/ZrACpH+pw+fTo6dOgAoLJ54fnnn8eAAQNQUlKCwMBAg/cEBAQ4JISY4uPjI/1BJCQkKLaVlJSY/UOS92tYsmQJdu3apdiek5ODq1evSq8bNmxo8x98QkKC1L9Afd+PHDkiLUdGRtr1h0t+j9xZRESEtJyXl+eya7b3/ZL//BQWFqKkpATBwcF2O74recrPljvhPbMc75V1XHW/7BpMPv74Y6v2Fz+wu06wds8996BPnz7YuXMngMoJ5syR92v4v//7P4Pt2dnZivKYONR8Vcm/hK3Z5s28sfOrXq83mC356tWrinFiiIg8mVOj0IULF3D27FlUVFQgLy8P7733Hjp37oygoCBnXobFfHx88OWXX6J58+YAgHHjxpl9j7nHcbOysqRgEhUVhZCQkKpfKLQn7xNV12ASHBwsDXvvLcHk1q1bKCsrU6yTV1CIiDydU/uYZGVlYd68ebh16xZCQ0ORnJyMOXPmOPMSrBYVFYXjx4/j8uXLmpPRqZkbMO327dvSb7zWPCpsTmhoqNFt1TWY6HQ61KpVC5mZmV4TTLRCCDv0EZE3cWow6dSpEzZs2ODMU9pFYGCgVDUxR6ti8vDDD2P16tUAgD///BMVFRUA7NeMA1R+Cffr1w/bt2832FZdgwmAahFMWDEhIm/CXkB2phVMnnnmGWlZ3inVnhUTAPj2228xa9Ysg/Wmmnm8ndjPJDMzE9OmTUN+fr6Lr6hqGEyIyNsxmNiZuiknPDwcsbGx0uuzZ89Kyx07drTruYOCgjBkyBDFOp1O57Jh6N2BvAPs0qVL8dVXX7nwaqpO3fEVYDAhIu/CYGJn6hAQExOj2ZTi7++PESNG2P38UVFRitfh4eHV+vE4dd8b+cBznkhrGgJxbiQiIm9Qfb+xHERdMYmJiUFISIjBeCz333+/Q5pY1HP5VOf+JQAMpgRw1VxB9lJYWGiwzlv6zxARAQwmdqcVTHQ6nUEIeeihhxxyfnXFpLoHE/V9zsvLc9GV2IdWMMnJyXH+hRAROQiDiZ35+/srXkdHRwOofExYFBsbi+HDhzvk/OoxYap7MHnqqacwevRo6bU3BRMx7LJiQkTehMHEwWJiYgAoJwScMWOG0waVq+7BJDIyEkuWLJFee1MwEedlysvLkx5BJyLydAwmDiYGk5kzZwIA6tSpg6eeesqh55R3djU2c3N1Im9e86ZgIn/aS6tTLBGRJ3L67MLVjRhM3nrrLSQnJ+Oee+5xeAdMHx8f6PV6AICvr69Dz+UJAgICEBQUhOLiYq8JJoGBgYp+Szk5OQyhROQVGEwcTHx8OCQkBBMmTHDKOX19faWmIzGgVHc1a9b0qmASEhLilZMUEhGxKcfBXNHHQ96Uw74HlcSA6Olf4PJgIq+Q8MkcIvIWDCYO8OWXX8LPzw+9evVCu3btnH5++bw+4lNB1Z0YTPLy8iAIgouvxnasmBCRt2MwcYAHH3wQmZmZ2LlzJ3Q6ndPPv2rVKgQFBaFOnTp4/vnnnX5+dyQGk4qKChQVFbn4amwnXjsrJkTkrRhMHKRmzZouCSUA0K5dO9y4cQOpqanVegI/OXl1wZZ+JoIgYPbs2WjVqhU2b95sz0uzmF6vR3FxMQBWTIjIezGYeKnw8HAEBwe7+jLchnwOI1uCyfz58zFnzhz8+eefeP311+15aRaTV3rcuWIiCAKOHz/u8TM5E5FrMJhQtVCVYHLlyhX84x//kF4fOXLEJf1U5GOYuHPFZNGiRWjfvj06dOigGFiQiMgSDCZULVQlmPz2228Gj11fv37dLtdlDXkwCQ4OdruKyfr16/HCCy/gmWeeAVA5geLZs2dde1FE5HE4jglVC/JgMnv2bDRq1AgJCQkWvffq1asG686cOYO4uDi7XZ8l3LlicuPGDYwdO9agQsIRaYnIWqyYULUgDyb79u3Dgw8+aPF7tYKJKyoB6mAir5hs3rxZ8zqd5cCBA5rNNllZWS64GiLyZAwmVC3IgwlgXT+RtLQ0g3Vnzpyxy3VZQx1MwsLCpCe/CgsL0bZtW5dVTi5evKi5nsGEiKzFYELVgjqYAJb3y3DXiomPj48iXOXk5ODo0aNOvy4A+PPPPzXXZ2ZmOvlKiMjTMZhQtaAVTNLT0y16rxhMatWqhbCwMADAiRMn0LdvX7Rq1QoXLlyw34WaoA4mWi5duuSUa1EzFkxYMSEiazGYULWgFUxu3bpl9n2CIEjBpEGDBmjWrBmAyrCyc+dO/Pnnn3jyySfte7FGaAWTBx54QLGPsSYVRygtLcXevXtRWFjIigkR2Q2DCVULYqVDzpKKye3bt1FSUgIAqF+/Pjp16mSwz44dOxShwVHUA6wBwMcff4zJkydL651ZMZk4cSJSUlLQrVs3o81irJgQkbUYTKhaqF+/vkE4saRiIu9f0qBBA/Ts2VNzv40bN1btAi2gVTGpW7cu/vOf/0jrL168iOLiYvz0008oLS116PWsWbMGAPDrr79K69q1a4e+fftKr1kxISJrMZhQtRASEoLvvvsOXbp0kdZZUjGRB5P69eujR48emvt99dVXVb5GUzIzM7FhwwbptXy6gaCgIGlMlYsXL2LUqFHo2rUrxo4d67Dryc7O1lw/c+ZMbN26VXpaiBUTIrIWgwlVGz169FBUF9588028+OKLJud0UQeTmJgYzf2OHTtmt+tUEwQBKSkp2LNnj7RO3fm1cePGAICMjAxs2rQJAPD111877JqMNRn17NkTPj4+iIiIAMBgQkTWYzChaiU6Olrx+p133sHAgQNRUFCgub+8qhIbGwsAGDlypMF+ly5dQr9+/fDqq6/afR6d7OxsnDp1SrFOHUyMjWKrHkrfXrSCSbNmzdCkSRMAQFRUFAA25RCR9RhMqFpRBxMA2Lt3LxYuXKi5v/w3fvHL9t1330WHDh0wfPhwPPzww9L2HTt2YN68eThw4IBdrzkjI8NgnaXBxFEDrmkFE/kTQpGRkQAqx1apqKhwyDUQkXdiMKFqJSAgQDGUu+jIkSOa+8v7UojNE40aNcKvv/6KDRs24K677jJ4z969e+1zsf9jSTBp3ry55nuN9QWpKq3HkuUdg8Vg4shrICLvxEn8qNoRO2bKGXtCR14xkX/Zilq2bGmwzt7D1VsSTIYPH44RI0agqKgIFRUV2L59O4DK67d0skJrqCsm99xzj6JiIlaXxGuoXbu23a+BiLwTKyZU7Wj9Bq/15a/eV6vS0qpVK4N18sdn7cGSYBIYGIj169dj8+bNirFWHFWtEINJSEgICgoK8PPPPyMgIEDaLg9x7GdCRNZgMCGC+YpJjRo14OdnWGBs1KiRwbpTp04pBkOrqtu3bxusMzYkPfBXkxPgmGCi1+tx+fJlAJVPA4WEhBhUodQVEyIiSzGYULXzwQcfAABiYmLQpk0bAJWdRLUGJBO/2LWacQDA19fXIJxUVFTg999/t9v1alVMgoKCjO7v6P4dFy9elEbDNdZMVLduXWn50KFDdr8GIvJeDCZU7UyfPh379u3D0aNHpcdbAcPKhCAI0he7vAqhtmrVKvTu3RudO3eW1tlzll91MHn00Ufh42P8r678Wh1Rrdi6dau0LB+wTm7gwIFSFWX16tV2f4SaiLwXgwlVOzqdDl27dkVcXJzi8WF1c05BQQHKysoAmA4mKSkp2Llzp+KR419++cVu1ysPJqmpqVi1apXJ/R3dlLN582ZpecCAAZr71KtXD7169QIAXLhwwa73g4i8G4MJVWt16tSRluUBoKysDLt27ZJeG2vKkWvfvj38/f0BOCaY+Pr6on79+mb3d2QwKSoqwo8//ggAiIuLQ9u2bY3uO378eGnZ0UP2E5H3YDChak0eTD755BOkpaUBAEaMGIFhw4ZJ20xVTERBQUHo0KEDAOD06dN2CwViE1Pt2rVNNuGI5CHK3k05+/btQ3FxMYDKAdW0Hr0WDR06VNouhhkiInMYTKhakzflfPnll0hKSkJJSQm+//57xX6WVEwAKPqZ1K5dW5q3xlaCIEgVE0vHAnFkxUQ+fklycrLZ62jXrh0A4Pjx4xxojYgswmBC1Zq8YgJU9jM5d+6cwX6WVEyAyoHG5ObOnWv7xaGyn4tYoVBfqzEhISFSk5K9w4C8H444d5Ap4mzMgiBg3759dr0WIvJODCZUrWl92csn7hPZGkxu3LhRpSdS5P1eLA0mOp1OqvA4MphozTuklpKSIi3v3r3brtdCRN6JwYSqNa0v1+vXrxuss7QpJyEhAU8//bRinVjxsIX8EWZLgwnwV5Cydx8TeWizJJh0795d6mfy888/2/VaiMg7MZhQtabVb0MrmFhaMQGA999/H4MHD5Ze5+Xl2XZxqKy4iCwJAiLxeu/cuYPy8nKbz69mbcUkMjJSuhatEWyJiNQYTKhak8/vIqpqMAGAmjVrSst37tyx/sL+Rxz6HdAe/t4Y+fXm5OTYfH41MZiEhIQgNDTUoveEhYUBqOwvQ0RkDoMJVXvTp09XvL527ZrBPtYGkxo1akjLVamY2COY2LOfiRhMYmJiLH6PGGDy8/Ptdh1E5L0YTKja++CDD7BkyRLptVbFxJpmFMB+FRP547nWBBP5TMi5ubk2n1+uvLxcminYmvshr5hwaHoiMofBhKo9nU6Hpk2bSq/l/TqAyvFNxC9XS9m7YuLn54e4uDiL31erVi1p2V7BRN5HxJpgIlZMBEGw66zLROSdGEyIoKxwyCsmH3/8MR588EGrjycPJvboY9KgQQP4+flZ/D5HBBNrn8gRyUMd+5kQkTkMJkRQBpPS0lJp2dpKidbxbA0mOTk5UsdVa5pxAGUwsVfnV/kTObb0MQHYz4SIzGMwIYIySMjZGkzsUTFJTU2VlqsSTGytmJSXl2POnDl46qmnkJWVZfWjwiJWTIjIGpbXhom8mL2DiT0qJvIncho3bmzVe+0RTN5++23Mnj0bAHD06FEMHDhQ2mZLHxOAFRMiMo/BhAiV43L4+PhAr9cr1ssrH9awR+dXW5/IAaoeTE6ePCmFEgA4cOAADhw4IL1mxYSIHIVNOUSofDJHq2riyorJyZMnpeXExESr3lvVYLJq1SqUlZUZ3R4fH2/xsVgxISJrMJgQ/Y89g4k9KiaHDx8GAPj6+qJdu3ZWvbeqweSPP/6Qltu0aaPYVr9+fTRp0sTiY7FiQkTWYDAh+h93qpgUFhbi1KlTAIDWrVsjJCTEqvdXdYC1M2fOAKj8/BMmTFBs69u3rzQxnyVYMSEia9g9mMydOxfDhg1DUlISjhw5otim1+vx3nvvoUePHujXrx9Wr15t79MT2UwrmFgbCETyQGNLMDl27BgqKioAAElJSVa/PzQ0FL6+vgCsDybFxcVS/5YWLVqgd+/eiu19+vSx+lpErJgQkTl2DybNmjXDP/7xD81RKtevX4+jR49iw4YNWLFiBb744gscOnTI3pdAZBN1MAkJCZG+3K3l5+cnhRpbgok81Hfq1Mnq98v7zFgbTM6fPy8NHd+8eXO0b99esd3aYCIPaayYEJE5dn8qZ9SoUZUH1hilcvPmzRg/fjwiIyMRGRmJYcOGYdOmTUhOTtY8VmlpqWKwK/G4WjPCVpX4NIb6qQz6i7ffI/UTODVq1KjSZ61RowYKCwuRl5dn9XHE/iUA0LFjR5uuo1atWsjOzkZOTo5V75f3L2nevDl0Oh1ef/11zJkzB+PGjUPt2rWtOl5wcLC0nJ+fr/leb//ZcgTeM8vxXlnHUffLx8eyWohTHxe+ePGiYk6SxMRE7N+/3+j+K1euxPLlyxXrRo8ejTFjxjjsGtPS0hx2bG/hrfdI/ZcmKChIMciZtcQv5NzcXKuPIw8HNWvWtOk6bD2/vIoZGRmJ1NRUPProoxgyZAjCw8OtvhZ5xejGjRsm3++tP1uOxHtmOd4r69j7flk6HpNTg0lRUZGivTk0NBSFhYVG9580aRIefvhhxTpHVkzS0tLQoEEDi1NddePt96hevXqK1+Hh4VY9FqsWERGBy5cvIz8/Hw0bNrSqw6jY5FGzZk00a9bMpvPXqVMHf/75J0pLSxETE4OgoCCL3nfz5k1puWvXrtI9sPVeyPuV+Pj4aB7H23+2HIH3zHK8V9Zx9f2yKpg8/vjjOH78uOa2xx57DNOnTzf5/uDgYMU/UgUFBSY7FwYEBDgkhJji4+PDH1wzvPUeyR+xBSr7RlTlc4rHq6ioQGlpqaJJw5zMzEwAQFRUlM3XIP88d+7csbgj74ULF6TlZs2aVfnPWt53p6CgwOTxvPVny5F4zyzHe2UdV90vq4LJxx9/XKWTJSQk4Pz581JzzoULF5CQkFClYxLZi1YwqQr5+/Py8iwOJnq9HtnZ2QAqg4mt1GOZWDrxnlgxiYqKsipMGcOncojIGnaPQmVlZSgpKYEgCCgvL5eWAeCBBx7A559/juzsbKSlpeGbb75RzL9B5Eo9evRQvPb396/S8eSVAmsGWZN3Vo2MjLT5/LYMsiYIAtLT0wFYN4OwKRxgjYisYfc+JjNmzMCvv/4KAJg5cyYAYOPGjahXrx5GjRqFtLQ0DB8+HP7+/nj00UeNPpFD5Gzt27dH06ZNce7cOQDAtWvXqnQ8W4OJ2IwD2LdiYon8/HwUFRUBsF8wCQgIgK+vLyoqKvi4MBGZZfdgsmzZMqPbfHx88Nxzz+G5556z92mJ7OK///0vkpOTIQgCRo8eXaVjyUdfzcnJsfh9WVlZ0nJVgokto7+K1RLAfsFEp9MhLCwMubm5rJgQkVmcXZhIJikpCTt27MAff/yBxx9/vErHsjWYuLJi4ohgAlT2M8nNzWXFhIjMYjAhUundu7fBMOy2sHUiPUcEE0uDkaOCidjPhBUTIjKHz00ROQgrJn8Rn8xhxYSIzGEwIXIQTwgmZ86cwbhx4/Dll18CcHwwKS8vN5hmgohIjsGEyEE8IZj06dMHa9aswWOPPYaioiKHBRP5E0rW3Asiqn4YTIgcxN2DyfXr13H16lUAkEKJo4JJbGystCwf8p6ISI3BhMhB3D2YrF+/XvH61q1bimASHR1t87nV5PMQXb9+3W7HJSLvw2BC5CC2PBUD/BVM/Pz8UKNGDZvPHxoaCl9fXwDaweSrr75SvM7IyJCCSXh4OAIDA20+txqDCRFZisGEyEECAwOlL3dbBliLioqyakZiNZ1OJ/XtUAcTQRDwyy+/KNbJg4k9m3EA7WDy/fffIyEhAXPmzLHruYjIszGYEDmQsWBginxm4aoSqzbq8xcWFqKsrEyx7ty5c9LjvHXr1q3yueXkwUQc6n/w4MG4dOkSZs+ejZKSEruej4g8F4MJkQOJwcTSismdO3ekQcjs0cdD7OeSk5MjTaYJaAelAwcOSMuJiYlVPrecuaYca+YSIiLvxmBC5EBiMMnLy0NFRYXZ/eVf2nFxcVU+v1gxKSsrQ3FxsbReKyj9/PPP0rK9g0lMTIzULMVgQkSmMJgQOZC1MwzLZzS2ZzABlFUSrYqJvDmladOmVT63nJ+fn9RvhcGEiExhMCFyIPlTNZY057gymMjZO5gAf32emzdvGsyZw2BCRCIGEyIHsnbEU3kwkffLsJWxYGLuWpo0aVLlc6uJn0ev1+PMmTOKbQwmRCRiMCFyIHkw2bFjh6IDqhZH9TEBLK+YxMXFISQkpMrnVpMHrVOnTim2MZgQkYjBhMiB5MHkpZdewuLFixXb09LSsGzZMmn8EGc15ZiqmNi746tI/gjyH3/8odjGYEJEIgYTIgeSBxMA2LNnj+L1yJEjMXXqVEyePBmAMpjYYywRSyomkZGRivc4on8JoByX5eLFi4ptd+7cccg5icjzMJgQOVC7du0Ur4uKiqTliooKHD58GEDlKKiCIEjBJDo6Gv7+/lU+vyXBpEGDBor39OzZs8rn1SIPQOpgwooJEYkYTIgcqHXr1ti8ebP0Wj7aqnyyPgA4evSoNNuvPZpxAOPz9ciXk5KSpOUuXbrgoYcessu51SIiIqRlVwYTQRAsGlOGiFyDwYTIwbp37y4tywc5k8/kCwCdOnWSlu0VTOQzHBurmPztb39Dnz59MG7cOGzdurVK8/OYIg8m4nxAImcGk4kTJyIgIADLli1z2jmJyHJ+rr4AIm8nn6VXPoiZOpjIOaJiYqzza2JiInbs2GGX85kiDyZqzupjUlxcjM8++wwAMHXqVAwdOtTuExYSUdWwYkLkYD4+PlJ/EXkwuXXrltH3jBgxwi7nNtfHJDg4GAEBAXY5lznuEEwKCwsVr9944w2nnJeILMdgQuQEQUFBAEw35QCVfVIyMjLQr18/u5zXXDCRb3c0U8HEWU058vsPAJ9++qnZsWWIyLkYTIicQGzOMdeU06dPH9SuXdtu5w0JCYGvry8A7aYcZwYTf39/hIWFaW5zVTApKCjgE0FEbobBhMgJxGAi/2LUasq577777HpenU4nhQ8xmFRUVEhNJ/LOsc5grGriqmACGHbEJSLXYjAhcgKxKcdcxaRr1652P7c6mMj7czizYgK4PpjIx5ERqR/bJiLX4lM5RE5grinnwQcfxIABA+wy2quaPJgIgqB4IsddgklBQYFTxhbRqpgwmBC5FwYTIicw1fk1JiYGX375pcPOLYaPsrIyFBUVKfqauEtTDlAZThyNwYTI/bEph8gJxIpJeXk5KioqIAiC1MfE0eNoqJ/MkfepcKdg4oxHhhlMiNwfKyZETqAeZK20tBSlpaUAKufFcST16K/yL2L5xHrOYCqY5OfnO/z87GNC5P5YMSFyArEpB6gMJvL+Jc6umLgymKhnMu7SpYu0zIqJfW3fvh1dunTBxx9/7OpLIbIKgwmRE6grJtevX5deO6LDq5w7BRN1xSQlJUVadkZAqE6PC8+cORMHDhzA5MmTFR2eidwdgwmRE8grJsXFxbh27Zr02l7z4hjjTsFE/RRQixYtpGV5WHOU6lQxOXfunLT8/fffu/BKiKzDYELkBOqKiTyY1KtXz6Hndqdgou5H0qBBA2n5xo0bDj9/dQkm8ievAGDdunUuuhIi6zGYEDmBumIirw5Up4rJwIEDpSHyFy9ejIYNG0rbnBFMqkvn1ytXriheb9261WkTJRJVFZ/KIXICUxUTZwaT559/XrFN3RnV0erXr4/du3fj8uXLGDt2rGJQNVZM7EcdTEpKSnDixAm7T3lA5AgMJkROIA8m6j4mzmzKkatRowYCAgIcem4tXbt2lYbe9/PzQ3R0NG7duuWyPiZ5eXkoKyuDv7+/w8/vLKmpqQbrbt++7YIrIbIem3KInED9uLAYTOrUqePwcGAsmDi7GccYsTnn1q1bKC8vd+i55MFE/jSUtz2Zo66YAAwm5DkYTIicQF4xKSoqkpotHF0tAdw/mIgdYPV6PZYtW4a2bdti0aJFDjmXvI+JvAnN24KJVsUkIyPDBVdCZD0GEyInkFdM0tLSpMqAo/uXAMaHnXe3YAIATz31FE6ePIlZs2YZPFliD/KKiTcHE1ZMyJMxmBA5gbxiMnPmTGnZGcEkODhYc727BBP5kzlyO3bssPu55MFEPhVAXl6e3c/lSuxjQp6MwYTICeTBRM4ZwUSn02mud5dgIq+YyG3atEnxurS0FB999BEWL14MvV5v07mqQzApLS2VOhI3atRIWs9gQp6CwYTICeRNOXL169d3yvnXr1+PXr16KdaFhIQ45dzmyEd/lVu1ahX++OMPAJVfqikpKXjyyScxY8YMm0cylfcxkc9R5E1jfFy7dg2CIAAA2rdvLwVT9jEhT8FgQuQEWhWTJk2aYNiwYU45/4gRI7Br1y7FuuzsbKec25zWrVujRo0amtvatGmDH374AfPnz8fBgwel9cePHzd5TEEQ8MQTT6Bbt26KZg15xaR27drSsjdVTOSfNyEhQZqfiBUT8hQMJkROoK6YRERE4OzZs05vTunXr5+0LC/zu5Kvry86d+6suU0QBMyaNQuXLl1SrL969arJY+7duxfLly/H/v378cgjj0jrxWASEBCg6BTsTcFE3vE1Pj4ederUAcBgQp6DwYTICdQVk6ioKPj4OP+v3/Lly1G/fn20bNlS0QnX1bp06aJ4ff78ealT7MmTJ7FhwwbFdnPB5OzZs9Ly3r17pWUxmAQFBaFmzZrSem9qypFXTBo2bChVhvLy8lBSUuKqyyKyGIMJkRNoBRNXaNiwIVJTU3Hq1CnFF7Or3XPPPYrXTZo0wcqVK43uby6Y5OTkKF4///zz+PnnnxXBRN58VB0qJoB3Dr9P3ofBhMgJ1E05zp6jRs7Hx8fokzquIm/K6d69OwCgW7duRvc3F0zS0tIUr9977z0MGTJEqowEBwcrgpk3BRNjFROAzTnkGRhMiJzAXSom7io8PBwLFy7E6NGj8eGHHwIA/P39pY6ballZWSgsLDR6PHUwASqrBWLFoDo05YSGhiIyMpLBhDwOgwmRE7hTxcRdDRkyBF9++SXatGkjrZN/qarJJ0JUM1dR8damHEEQpKac+Ph46HQ6RVMOHxkmT8BgQuQErJjYRv6lqmYqfGhVTOSCgoLg7+8vBUZvCSYZGRlSPxqx87A83DGYkCdgMCFyAlZMbGOqYmIsmJSUlCA9PV16rTWInThMv9ic4y1NOfL+JfHx8QCUsyhfu3ZNMZYLkTtiMCFyAlZMbKMOJvIvWWPBRN7EM3r0aHz55ZcG+4hBUQwm3lIx+b//+z9puWnTpgD+CigAMH/+fISFheH55593+rURWYrBhMgJ1MGEFRPLqJty7rrrLmlZa6I6QBlY6tevr1kxEYOJ2M8kLy9PGsbdXaxZswbJyckGY7gYk5mZiY8++ghA5c/bww8/DMBwksSKigq89957HNOE3BaDCZETqJtyWDGxjLpikpycLA1Mt2XLFs3J/OT9Sxo0aIB69eoZPB6trpiUl5e73Rf1uHHjcPjwYYwcOdKi/T///HMUFBQAACZPnozY2FgAlZ9VPi+QyNyw/kSuYvdgMnfuXAwbNgxJSUk4cuSIYtvSpUvRuXNndOvWTfqPqDrw8/NTvGYwsYy6YpKQkID7778fQOVAYj/88IPBe9TBxN/fX/qSFqmDCVAZen799Ve7XXtViAHDGvKB1R588EHFNnlzjujw4cPWXxiRE9g9mDRr1gz/+Mc/jE7nPmjQIOzbt0/6j6g6UP/GzqYcy6grJpGRkZg0aZL0Wmt0WHUwAQw7wIqdX+WPDP/++++YNm1a1S/aDtRPFWlVhtTy8/Ol5Vq1aim2MZiQJ/Ezv4t1Ro0aVXlgv6ofurS0FKWlpYp1fn5+CAgIqPKx1cS/+Jb8A1Bd8R5Zx9T9CgsL432UMXav1AGuVq1aSE5ORq1atZCbm4sffvjB4D3yykFcXBz0ej3q16+v+CIODAyEXq83mNX48OHDbvHnou4/k5OTo5h0EDC8Z/Ini0JCQhSfQ93PBAAOHTqk2Cc3Nxfjx49HUFAQvvjiC80ZsT0V/+2yjqPul6Xzg9k9mJiza9cu7N69GzExMZg8eTJ69epldN+VK1di+fLlinWjR4/GmDFjHHZ95sY/IN4ja2ndL/mXJ/1Ffa/Uv5gUFxfj5s2baNy4MY4dO4b09HScO3dO8cuKOBOxn58fiouLkZqaalBBKCkpQWpqqmaHV2Odap3pt99+U7w+efKkVP1RE++ZfIyS7OxsxedQBzAAOH36NE6ePClte+edd7B582YAwN///nc89dRTVfsQboj/dlnH3vercePGFu3n1GDSt29fjBw5EuHh4Th8+DBefvllREdHK0Z6lJs0aZLUs1zkyIpJWloaGjRo4JJZXz0B75F11PfriSeewLJlyzB16lTN0np1ZuxnSz0kfevWrVGvXj00adIEx44dgyAICAgIUNxPcQyTevXqISEhAUDlLzSrVq0CAPj6+mL06NGIj483aOIJCgpyyp9NRkYG3njjDdx1112YMmWKwXb1cPshISEG16W+Z/Lfblu2bImQkBDpdfv27Q3OIQgCCgoKpH9/5RWl/fv3491337Xps7kj/ttlHVffL6uCyeOPP260J/djjz2G6dOnm3y/+I8EANx7773o378/9uzZYzSYBAQEOCSEmOLj48MfXDN4j6wj3q+PPvoIr7zyijRUOBlS/2ypKx1RUVHw8fFRVA+uXbsm/SZWVFQkzQcj/0f1gQcewIkTJ3D16lW0adNGer96huU6deo45Wf7zTffxOLFiwFUdrrt0KGDYrt6jJa8vDyj1yXeM7GPiU6nQ2hoqOJnzFi15cKFC7j33nsBQNFUlJOT45V/x/lvl3Vcdb+sCiYff/yxXU/OHxCqTnQ6HRo1auTqy/Ao6gAndlqVVzrkX+LyZfmXsU6nQ9u2bdG2bVvF8dQDq9nyNIwtxIkKAWDTpk0GwUTd1JednW32mGIwCQsLM7hvLVq0QGRkJLKystC4cWOpuev8+fPSPvLqVE5OjmUfhMgB7J4MysrKUFJSAkEQpLEBxHbcPXv2ID8/H3q9HocPH8aWLVvQtWtXe18CEXmRsWPHAgB69+4trTMWTORt4loDq6mpx/dwVjCRkw+fL6pqMFELCQnBzp078eGHH+K///2vtP7cuXOKfUS5ubnmL5zIQezex2TGjBnSWAAzZ84EAGzcuBH16tXD1q1bMXv2bFRUVKBevXr4+9//jnbt2tn7EojIi6xYsQITJkxQ/BJjbcXEmPHjx2P58uXSmEslJSUoLy+3y1OFpohPFQHArVu3FNsEQTDodPjHH38gMzMTJSUl+PHHHzFgwACDZi5TwQQAOnTogA4dOqC4uBg6nQ6CICgqJvJ+LepOx1oqKipQXFyM0NBQs/sSWcPuf/uWLVtmdNu8efPsfToi8nKhoaEYMGCAYp08dMi/xLXGMDElJCQEhw4dQv/+/bFjxw4AlVUT9Ze+vUVHR0vBRB1Cbt++bTDR3oIFC7B06VLodDoUFBRg6NChBkPVmwsmoqCgIDRo0ABXrlxRVEzk46AAlU9AqUcsFh04cAAPPvggcnJysHv3bnTs2NHkOYmswU4eRORx6tatK/WjMNaUY0kwASr7n8i/zJ3RnFNWViYtnz17VrHt1KlTmu8pLCyUru3bb79FVlaWtK20tFQ6prlgAvw1wV92drZ0HHUwWbduHe6++27MmTNHsf706dPo0qUL0tLScOfOHYvn8iGyFIMJEXkc+TDz8mBy/fp1adnY6NNa5F/m6i9oR5D34cjMzERmZqb0+pdffrHoGFu2bJGW5ddsSTBJTEyUlsWqifpzT5gwAb/++itmz56tuN7PPvtMsZ8z7hdVLwwmROSRxH4mN27ckKoF8i9Q9Rgopsj7STi6YiIIgkHnUnmTyqFDhyw6zvfffy8tWxtMxIqJ/NzykWPV5J1vb968qdjmig7D1Ym7zXrtDAwmROSRxAHHBEGQhjIQv1z9/PysGlLdmcFEfDJR7o8//pCWxYqJuU6lW7duRXl5uXRMkSXBpEmTJtLy5cuXDY6hdc0idWddBhPHmTp1KmJiYhTVseqAwYSIPNIjjzwiLT/99NM4e/asFExq1qxp1SB2zmzK0XoU99NPPwVQOVjctWvXAACdO3c2eZy8vDzpUWNrg4l8PJ1Lly6hoKDA4mAiH/oeMBylluzj6tWrWLZsGTIyMgw6f3s7BhMi8kiDBw/G008/DaCyM+nGjRulAdO05oYxxZkVE61gsnfvXhw5ckTRv0QrmHTu3Bn9+vWTXovNKlUJJp988gnCwsIUx1APfilv5mHFxDmqc98dBhMi8liTJ0+Wlg8fPix9gXpKMJGf9z//+Q9++OEH6bU4VLxo0aJFOHjwIHr27Cmts7ViEh4ebjBbsah79+7Izc3F/PnzpXVsynG+6jwTMoMJEXks+WR1Bw4cQFFREQDrg4mrmnJmzpwpBYR169ZJj976+fmhR48eiveJTxnJnzaytWICwOj0CBEREQgLC9O8JwUFBQZNNwwmjiH+LFdHDCZE5LH8/Pykwb3kY5h4SsUkJiZGGnK/sLAQN27cAFBZLalRo4ZihNsWLVoAUAYTWysmgPEp6MX3y++hWIlSV0sABhNHqc59dxhMiMijderUyWCdetZgc+TBxNEVE/kEebVq1cLEiRMN9hH7kXz11Vdo3bo1/va3v6FVq1YAlMPxO6JiIr5fq2KiFUyq8xeoI1Xn++rYCSGIiBwsOTnZYF1VmnKcWTGpVasWkpOT0bp1a8WIr3379gVQWTk5efKk4v3yiokYFFwZTFgxcQx1U44gCFY9aebJWDEhIo+mNRGopzTlhIeHQ6fTYf369XjkkUfQokULPPXUU5phS36t4lw+jqiYiPdOqylH/agwUHm/quMgYI6mrphYMrGit2DFhIg8mjg0vZyndH4VA0bz5s2lsUwsERcXh9zcXKSnp0MQBJuCSb169TTXW1sxqaioQGlpqVUD2pF5Wp2Mq8s9ZsWEiDxaeHg4/PyUv2NVpY+Js5tybCE25xQXFyMnJ8emYHL33XdLTUZylgYTefirzv0hHEXdlFOdxjVhMCEij6bT6RAdHa1Y5ylNOVUNJkDlaLG2BBOdTodt27bh9OnTivWmmnLkwUTeFMR+JvanDnsMJkREHsSewcTZT+XYQh5Mrl69qhiZ1dJgAlSGk4YNGyrWWVoxkT9uzGBif6yYEBF5sKoGE/mkf47+khU7rAYFBdncZ0AeCn755RccOHAAABASEmJ28j+14OBgxWsxkAQGBsLX1xeAYTDx9/dH3bp1pfcwmNhfdR7IjsGEiDxeVYMJ8FfVxJG/md68eRPnz58HAHTo0MHm4zRv3lxafuONN6QvrYkTJ0phwlZiMNHpdNJ9VDflREdHO/UR6+qITTlERB5MHUys7fwK/PWF7Mgv2b1790rLKSkpNh9HHAVWzt/fHy+//LLNxxTJJ/AT70l+fj4EQZAeF46OjlZUZtj51f7YlENE5MHsUTER56y5ffu2oh+IPe3Zs0daVs+FY43atWsjMjJSsW748OFo0KCBTceTV2/k/VfkwSQnJwfl5eUAKu+3OEcRwIqJI7Aph4jIg9kjmIhBoby8HN999509LsvA7t27AQC+vr7o0qVLlY4lb84BgF69etl8rHXr1mHs2LFYunSpou+IeB/z8/OleXkAw4qJN39pvv7662jRogV27drl1POyKYeIyIPFxMQoXtsSTEaPHi0tr127tsrXpJaVlYU//vgDQOUYIrZco1yzZs0Ur7t3727zsRISErBmzRo88cQTivVixUSv1yM1NVVaX12CSXZ2Nt544w2cOXMGffr0ceq52ZRDROTB1BUTa59MAYAuXbpIo6Fu27bN7s05x48fl5ZNDTlvqcTERMVrrX4nVSXv4Hrx4kVpuU6dOtUimMirRM7GphwiIg+mDibyDpyW8vHxkaompaWlWLJkiV2uTSQPJlrz+1hL/pn9/f0dMsGbvKpz4cIFxbmrQ+dXVwYTVkyIiDxYnTp17HKcmTNnSqFmwYIFdv3CPXbsmLTcvn37Kh9v5MiRUofdr7/+usrH02KsYlJdOr86M5i8+uqriI+Px6ZNmwCwjwkRkUdTDxJmq8TERKlqkpGRYdcvfLFi4uPjg9atW1f5eBEREdi9ezfOnz+PgQMHVvl4WkwFk+rQlCMOhifS6/UOOc+dO3cwb948XLlyBYMGDQLAphwiIo/36aefolOnTti4cWOVjjNu3DhpWRwMrapKS0tx6tQpAJV9QewVpGrWrKkYBdbe5E058qYobw8maWlpeOaZZ/CPf/xDsT4vL88h58vMzDRYV52bcvzM70JE5P4eeeQRPPLII1U+jnwcjxs3blT5eABw+vRplJWVAbBP/xJnUXewFdWpUweCIEivvSmY7N69G8OGDVNMtijKzs6Wms/s6fbt2wbr2JRDREQAID2ZA9gvmFy6dElabtmypV2O6QxjxozBvHnzDNar5+Txps6vb7zxhmYoASqDiSOog4kgCGzKISKiStHR0VIH2OvXr9vlmPLfdm2dUdgVAgIC8PLLL2P+/PkG25w5I7MzqfuVyDkrmBQXFysqUsBf97ioqAhnz551yHW4CwYTIiIZX19f6VFce1VM5F/c8g6lnuLxxx+XlsWBxoKDg6XZkbOyslxyXY5gKnw4aqoCdTDRCkf5+fkoKytD+/bt0bx5c3z88ccOuRZ3wGBCRKQiDst+8+ZNuzyJIS/D2zL4m6vVrl0b3333HR5++GG88847ACpnH65duzYASJP7eTpBEEyGLGdVTLQCcV5eHvbu3StVSyZPnuyQa3EHDCZERCpiP5OKigq7fOl6ejABgEGDBuGLL75QjMEiBpPbt28bND14osLCQpSWlhrd7qxgotWEmJ+fj7S0NIec390wmBARqcgnsrNHc468KcdTg4kWcWC7srIy3Llzx8VXU3XmmqRcGUwA5ei73ozBhIhIxd7BRF4x8cQ+JsaIFRNA+5FXT2MueDirj4mxnzlxEkhvx2BCRKQif2TYHk/meENTjhZvCybyisnIkSPh7++v2O7qiol8WgNvxmBCRKTCphzLeHMwSUpKwtGjR7FhwwZpnbOCiXzE4Zo1a0rL8mkB1KHJmzCYEBGpyIOJvSsm3tqU4w1P5siDR2RkJNq2bYuhQ4dKMzc7IpgIgmAQTH7++WdpuWPHjprvCwoKsvu1uAsGEyIilfj4eGl58+bNJp/UsIS3NuXIZ3WWf7mWlpZi9+7dHjcirLxiEhkZCaBy0kVxUDxHBJPc3FxUVFRobuvUqROmT5+uua2wsNArnoTSwmBCRKQSExOD+++/HwCQmpqKzz77rErHE5tyfHx8pEHJvIGxppypU6eiZ8+eGDZsmAuuynbyYBIREWGwnJmZafcwoDWBn+jbb79Fw4YNNbdVVFRUOTC7KwYTIiINr7/+urS8aNGiKh1LrJiEhYVJzQLewFgwWbVqFQBgx44dHvVbvbopR9SgQQMAlcHlP//5j93Od/36ddx9992a2+Li4lC3bl3FPVbzpqkA5BhMiIg03HPPPdKEe2fPnq3SF6wYTLypGQewrPOrox6xdQStphwAePnll6XlF154wW79aVauXGl0wkBxFuqoqCij7/fWif0YTIiIjKhfvz4AoKSkpEpfsOJvtt4cTIx9WXtSp1hjweSBBx7AxIkTAVT2n/n999/tcr6rV69Ky8nJyYptYjCpVasWfH19Nd/PigkRUTVjr/FM5E053iQgIEB6nFWsmKj7Pdy6dcvp12UrMZj4+voa/Fm1adNGWrbXZ5JXmb766ivFNjGYyOckUmPFhIiomrFHMCktLUVZWRkA76uYAMr5cgAYDE3vSRUTsY9JZGSkQV+gmJgYaTk9Pd0u55N3fFU32YjBRGubiBUTIqJqxh4DrXnro8Ii8ZHhrKwslJeXIy8vT7Hdk4KJWDGRN+OI5MHEXhUTMZgEBAQgNDQU99xzj7StadOmZs/HigkRUTVjj4qJtw6uJoqOjgbw10BhnloxycjIkEKVVtOJ+DkB8xUTvV6PVatWGTTPqInBJCoqCjqdDitWrMCUKVOwc+dORb8SYx2LtSomgiDgiy++wLfffmvy3O6MwYSIyAhWTMyTVxJu3rzpscFkx44d0vJ9991nsN2appzvv/8ekyZNwoMPPoht27Zp7iMf8VUMQq1bt8ayZcvQu3dvxb4vvfSStPzkk09Ky1oVk88++wwTJkzAsGHDcOTIEZPX6a78XH0BRETuyh4VE2+dJ0ek/sLW6/WK7Z4STLZs2SItP/DAAwbba9euDZ1OB0EQzDblLF++XFp+9NFHcfPmTYN9CgsLUVJSAsD0I8EA8OyzzyI/Px8dO3ZEUFAQlixZAkA7mIhPDwHAd999h6SkJJPHdkesmBARGREbGystsylHmzqYqCsmnvBUjl6vlyobYWFh6NKli8E+fn5+UoA4dOgQVq5cieLiYs3jyQNoenq65pDzpjq+qsXExOCDDz7AY489pvgZUjflXLp0SfHaU/ugMJgQERkRFBQkdYRkU442c8HEEyomv//+u3SdvXv3RkBAgOZ+8s/62GOPYeDAgSgvLzfYTx3G5JPyieT9RswFEzn5z5A6eKxZs0bxWh1UPAWDCRGRCWI/k+vXr9s0+mt1a8rxxGBy5coVadnYbL6AsgMsAPzwww+YPXu2yeMBlRNBqskrJqaGnVczVTHZs2eP4jWDCRGRFxL7mdg6+mt1q5hoPS7s7vPlWNqsIv+somXLlknLgiDg8uXLBsEkLS0NJSUlmD9/Pt58802cPHnSqqYcOVMVk4sXL5p87SkYTIiITJD/lmzLb//VvY9JWVmZQVhxN5ZWL7SCSUZGhjSA3hNPPIHGjRtLr0U5OTlYvXo1XnnlFfzzn/9E27ZtpYkOAeuCibGKSUVFBVJTUxX75ubmKiYm9BQMJkREJsi/CGzpTOjtTTnh4eFSnwytYAK4fwdYS6sX4vD7ardv34YgCFixYoXm9uzsbFy4cEGxTv4YsT0qJteuXTMIRIBnNufYNZhcvnwZzz77LPr06YPevXsbzMJYXFyM1157Dd27d8fAgQOxdetWe56eiMjuTJXOLeHtTTk6nU6qJGg15QBw+9/aLQ0mxp7CycjIMPkZc3JyTG63R8XEWLNNtQ8m+fn56NmzJzZs2IAtW7YgOjpa0TFo6dKlyMnJwebNmzF//ny89dZbuHz5sj0vgYjIruRhorCw0OL3CYIAQRCQlpYmrdMa6twbyIOJ+skQAB7VlGMqJDzyyCPS8l133SUtZ2RkGDSjyGVnZ5vsn2RN59egoCBpHh956JUHEPnjzuL68vJyDB06FO3atTOo3rgbuw6w1qZNG8UMjGPGjMGECROk15s3b8Zbb72FsLAwtG3bFikpKdi2bRumTp2qebzS0lKDmSr9/PyMPspVFeKgQOrBgegvvEfW4f2ynDvfq+DgYGn5zp07Fl2jIAiYOHEiVq9eLXX89Pf3R5s2bez2Gd3pnonz5RiTnZ3t0us0d6/kj+5GRkYa3a9Vq1bYt28fCgoKcOrUKTz33HMAKke8NRU8zFVMIiIirLo/oaGhyM/PR0FBgfQ+edjo1auX9Ijy+fPnodfrsWzZMmzcuBEA8OKLL2Lt2rVGj++ony0fH8tqIQ4d+fW3335DQkICgMrEnJmZicTERGl7YmIiTpw4YfT9K1euVIygBwCjR4/GmDFjHHPBgOK3G9LGe2Qd3i/LueO9EkfnBCofAzX1m7Ho+PHj+OKLLxTr2rRpY7dZaeXc4Z6pZ+JVu3jxokX3zdGM3StxjJrAwECzHZzj4uIAAH/88Ye07uzZs5pf4vHx8UhNTUVRUZHRc/v7+yM3N1ezb44xwcHByM/PR2pqKg4ePIi6devi5MmT0nZ5NefEiRNITU3Fpk2bpHU7d+606M/D3j9bjRs3tmg/hwWTtLQ0fPjhh5g7dy6Av0qg8rJoaGgoioqKjB5j0qRJePjhhxXrHFkxSUtLQ4MGDSxOddUN75F1eL8s5873qn79+tJyUFAQ4uPjzb5H/HdPrkePHha911LudM+GDRumOVaHKCAgwK6f3Vrm7pUYCmrXrm3xdbZs2VJarqioUPT3CA8Px+uvv45du3ZJAeDatWuax4mLi7P4C1vUv39/fPHFFygoKMCzzz6LNm3aKCbtGzBgAOrUqSM1McXHx0szJwOVgcnU53T1z5ZVweTxxx/H8ePHNbc99thjmD59OoDK9raZM2di2rRp6NSpEwAgJCQEQGWbmNh5p6CgQFEmVQsICHBICDHFx8fH5X/J3R3vkXV4vyznjveqRo0a0nJxcbHZ68vPz8eXX35psP6+++5zyGdzh3s2adIkREVFYdKkSZr9SfLy8lx+jYD2vRIEQTHLr6XXKX90OCMjQ1FpOX78OBo2bIjffvtNWmesqceWL/+FCxdi//79uHz5Mg4dOoRDhw5J22JjYxEaGormzZsjIyMD169fR35+vqL6ERcXZ9E5XfWzZVUw+fjjj83uk5OTg+nTp2P48OEYOXKktL5mzZqIiorC+fPn0b59ewCVbWJNmjSx7oqJiJxI/KUKsOypnF9++UVzOvp7773XrtflTvz8/DBixAjs27cPCxcuNNiem5vr/IuyUEFBgdSX0ZqnY+T9auSdX319faVB+SIiIswep0GDBtZcLoDK61y4cCGGDRtmsE3s6tC8eXPs378fAHDmzBlFxUZrGH13YvencmbOnImuXbsqZjgUDRgwAJ988gkKCgpw8uRJ7NmzB/3797fnJRAR2ZW1jwvL59QRB2cbPHiwYqZibyXv2yDnzk/l2Do0vDqYiE+Y1q9fH35+lb/zh4eHmz2OvKnQGgMHDlT07QkICMC5c+ekYNiiRQtp286dOxXvdec/D8DOwWT37t04ffo01q1bh27dukn/iaZOnYqaNWvi/vvvx0svvYQXX3wRjRo1suclEBHZlbXBRN7BddGiRUhPT1e0/3uz1q1ba65354qJrUPDBwYGSgOuXbx4UerDIe+74aiKCVBZpZozZ470eu7cuUhMTJTCSvPmzaVtGzZsULzXmo62rmDXzq+DBg3CoEGDjG4PCgrC//t//8+epyQicihrxzGRB5OYmBiDid+82d13343k5GQcOnQI8+bNwyuvvALAvYOJrbP8ApVVk7y8PFy/fl1aJw8mllRMbA0mAPDCCy/g6tWr8Pf3x1NPPaXYJg8mR44cUWxz94qJQx8XJiLydNb2Mbl586a0rDW3ijfz9fXFTz/9hBs3bqBevXpSMHHnL0JbKyZAZTBRD1ZmrmKSmJiI8+fPS69tbcoBKn/ZX7p0qea2hIQEBAUFaY5W6+4VE9d3kyYicmNVacqpbsEEqGxiaNCgAXx9faUnmty5YmJrHxMAmtUwefcErWDSrFkzxeuqVExM8fPzw/z586WfX3kfpzt37rj1jM8MJkREJpgKJoIgYPr06ejWrZv0m7MYTPz9/S3qY+DNxD4Y7hxM5ON7WDtlQGxsrME6c0056mBibRiyxqxZs3D79m0cOHAAp0+fRq9evQBU/tzaMu+TszCYEBGZIG/KUfcx2bNnD5YsWYL9+/dj3LhxAP4KJjExMWZHRPV2tWrVAuDeTTnyaxOv11LyQdZE5ppymjZtqnjt6HFCgoKCcM8996BGjRqKMXncuTmHwYSIyAQfHx8EBQUBMKyYnDt3Tlo+dOgQKioqpIG2qmMzjpr4RV9QUOA2Y2eIVa4uXbrg7NmzimAiVngsJZ8bTiRvmtGqmMTFxaFVq1YAoJhLzhnkn8+dwyI7vxIRmREaGori4mKDYCIGFlFmZiYqKioAMJgAhl+E7jC78vbt27FkyRIAwIwZMxTXZG3FRB1MYmNjFT8ToaGh8PPzU4SyiIgIbNmyBT/88AOGDh1qy0ewmamKyR9//IGlS5di7Nix6Ny5s1OvS40VEyIiM8R+JuqmHPUTD9W946ua/IveXX5D/+mnn6TlnTt3VqliEhMTo+iDpJ5/RqfTSSOdiyIiItCwYUNMnDjR6X2QTFVM+vfvj0WLFqFLly5OvSYtDCZERGaIXz7qiol6/hMGEyX5F6G7dIC9deuW4rX8C1peUbCETqdTNNdovV89oaMlY5s4iqmKydWrV6VlVze7MZgQEZkhn4RU/pilOpj8/e9/l5a1ntiobuQVE3cJJvLwGB0dLQWTkJAQaSh5a8i/7LXmSOrbty9GjBgBoPIJHFf+XMiv1VQFy9iEg87CYEJEZIZYMdHr9SgpKZHWq79s5bO8smLink058ll2o6KipD9Da5txRPLJavv166e5zxdffIHly5dj79698Pf3t+k89qBuylm7di1++eUXFBUVKfaTP0LtCuz8SkRkhnpYerGDo7HfLP38/JCcnOyMS3Nr7taUIwiC4kmqkpISKTBZ2/FV9Morr+DIkSMoLy/Hc889p7lPcHAwJk+ebNPx7UleMZk5cyaAysn/Nm3apNgvKysLdevWdeq1yTGYEBGZoR5kTXySw1gw+fnnn5GQkOCMS3Nr7taUk5GRoajc5OTkSK9trZiEhoZi69atdrk+R9P6jKWlpXj77bcV61wdTNiUQ0RkhrH5crSCyfTp09GpUydnXJbbM9f/Qk4QBMyYMQPdu3c3mH/GXuTVEqDyC1jsM2RrMPEkxjr37tixQ/E6OzvbGZdjFIMJEZEZ8orJnTt3cObMGVRUVGhWAdSPh1ZnYWFh0rK5YLJ582YsXrwY+/btM9okUhVFRUVYuHCh0e3VOZioMZgQEbk5eTAZOnQoWrRogWnTpmlWTBhM/mJNMNm9e7e0/O233xpsLy8vx549e2xuElq4cCE2bNhgdHt1CCbqzyjOnaPm6s6vDCZERGbIg8mNGzcAACtWrFCM/SDSGqa8urImmNy8eVNaDgwMNNj+3HPPoUePHujXr59NM+Nu2bIFQOXkilqPBdva+dWTqCsmc+bM0dyPwYSIyM3J+5iYExwc7MAr8SzWBBN5yNN61HrRokUAKh/JzszMtPgaysrKcPDgQen4Xbt2xdSpUw32qw4Vkxo1akij044aNQpJSUma+zGYEBG5OXnFxJh7770X27Ztc8LVeA51MMnMzMScOXM0n2L5448/LD7u2bNnLdqvuLgY7du3x3333Set6927t2YIqQ7BxMfHBzt27MCSJUuwfPlyBAUFaT594+pgwseFiYjMaNKkicntY8eOxZo1a5x0NZ5DHUz+9a9/YcGCBfD19cXp06eRmJgIoPIxXvlQ8bdv3zZ53HPnzlk0p8uuXbsMAk+vXr2wZ88eg32rQzABgKZNm6Jp06bS60aNGknNkyJ2fiUicnPmBktz5fwn7iw4OBg6nQ5AZTBZsGABAKCiogLvv/++tN+pU6cU7yssLFSMRqruU2JpxUTsVyIKCwtDUlKSZn+S6hJM1Bo3bmywjsGEiMjNRUZGonnz5ka3V4eOk7bw8fGRmsHy8/MREBAgbVu/fr00WZw6mABQ9CNR909Rj0dijLrJ6MUXX4S/v79mCKmuf4ZawcTVTTkMJkREFrjnnnuMbmPFxDixOSc7O1sxa+2NGzewa9cuANpBQ96co35E2JKKyfnz56WB2nr06IEzZ85IkyyyYvKXRo0aGayTDzznCgwmREQWaNu2rdFt8on9SEl8RPXmzZvQ6/WKbWIg0Rrp1VQwOXfunNkvzv3790vLffv2VUyeV107v2rRqphUVFSYfYrKkRhMiIgskJKSIi0PHToUHTt2lF5b8tROdSXvAKsmdng9f/68wTZTwaSwsBDXr183eV754HfiI7IiVkz+Uq9ePc31rpzbiE/lEBFZICkpCbNnz8bBgwfx7rvvQhAEJCcno0aNGnjsscdcfXluy1QwycjIQEVFBS5evGiwzVQwASqrLHFxcUaPLZ/TSB0c2cfkL82aNUObNm1w8uRJxXpjE1Q6A4MJEZGFXn/9dcXrW7duQRAERadOUjIXTK5du4bS0lIAlaOylpWVATAfTK5du2byvPKmCPU1qENIhw4dEBERYfJ43srX1xcHDx7EmTNn8M0332DZsmWIiIhARUWFy66JTTlERDby9/dnKDHDXFOOvBmnc+fO0rL8qRytYHL48GFs3LgRxcXFmsc2VTFRD83+/PPPG73G6iA0NBQdO3bEG2+8gZs3b+LUqVNo166dy66HwYSIiBxGK5iIHVEzMjIUwUT+5JO5ismCBQswdOhQvPLKK5rnNRVM/P39FcPejx492tzHICdiMCEiIodRB5N69eqhfv36ACorJvIncuTBZOfOnVJzgqn+DgsXLlQ8hiwy1ZQDAEuWLMHgwYPx008/KZ7YIddjMCEiIodRh4LGjRsjOjoaQOV4GfLB1Tp06CBNmHj79m2MGjUKgiCY7Yh54MABg3WmKiYAMHz4cGzcuNGioe3JuRhMiIjIYbSCSZ06daTXmzdvBlD5pEyjRo0we/Zsads333yD5s2bY8mSJSbPsXHjRgCVI72++uqrSE9PNxtMyH0xmBARkcOYCybiQGmdO3eGj48PXnjhBQwZMkTarh4VVt43RPTdd98hKysLDzzwAObNm4fp06dLTTk6nQ7BwcF2+zzkeAwmRETkMKaacuTuvfdeadlU80rLli0N1p07dw5//vmn9HrDhg1SxSQ0NFSaSJA8A4MJERE5jLmKiUgeTBISEoweT2ubXq9XPMUDQBFMyLMwmBARkcNYGkzkY5iYCibGJkxUN/mITTmmxlEh98RgQkREDiMPBn5+fqhfv75BU85dd92lGHnVVDApKirSXK+ecZgVE8/FYEJERA4jH2U1Pj4evr6+BhWTt99+W/E6IiLC6NgivXv31lyvrpiIMz4zmHgeBhMiInIYecWkcePGACorJC1btoSvry8WLlyI/v37G7xPr9cbrJs5cyaGDx+Op59+GmPHjsXChQulbeqKidb5yTNwEj8iInKYhg0bIjY2Fjdv3kSfPn0AVA4Jf+LECdy+fRuxsbGa75NPIjdw4EBMmzYNffv2hY+PD95//30AwKZNm6R9rl+/rnkcVkw8D4MJERE5TGBgII4cOYJTp06hZ8+e0no/Pz+joQQAkpOTcejQIQDAfffdh0GDBhnso9WJVo3BxPMwmBARkUPFxcUhLi7Oqvd89NFH6NatG6KiojBjxgzNfSwJJmzK8TwMJkRE5HY6dOiA9PR0BAYGws9P+6tKa6A2NVZMPA87vxIRkVsKDQ01GkrE7eaGm2cw8TwMJkRE5LHMNecwmHgeBhMiIvJY5oIJ+5h4HgYTIiLyWOb6mbBi4nkYTIiIyGOxKcf7MJgQEZHHatGihcntbMrxPAwmRETksYYMGWJyOysmnofBhIiIPFarVq1Mbmcw8TwMJkRE5LF0Oh06duxodHtkZKQTr4bsgcGEiIg82meffYbAwEDodDrs2rULb7zxBpo1a4ZJkyZJMxqT5+CQ9ERE5NFat26N06dPo7S0FM2aNUOvXr3w2muvufqyyEYMJkRE5PEaNWrk6ksgO2FTDhEREbkNu1ZMLl++jPfffx+///47BEFAx44d8eKLL0oD4MyePRvbtm2TJmWqW7cuvvrqK3teAhEREXkwu1ZM8vPz0bNnT2zYsAFbtmxBdHQ0Zs+erdjn8ccfx759+7Bv3z6GEiIiIlKwa8WkTZs2aNOmjfR6zJgxmDBhgs3HKy0tRWlpqWKdn58fAgICbD6mMXq9XvF/MsR7ZB3eL8vxXlmP98xyvFfWcdT98vGxrBaiEwRBsOuZZb755ht88803WLVqFYDKppx9+/YBAOLj4zFjxgzcfffdRt+/dOlSLF++XLFu9OjRGDNmjKMumYiIiBzA0ke3HRZM0tLS8Nhjj2Hu3Lno1KkTAOD06dOoV68egoODsXPnTsyfPx9ffvkl6tatq3kMZ1dM0tLS0KBBA4tTXXXDe2Qd3i/L8V5Zj/fMcrxX1nHU/bL0WFY15Tz++OM4fvy45rbHHnsM06dPBwBkZGRg5syZmDZtmhRKAOVkSw888AA2b96MgwcPYvjw4ZrHDAgIcEgIMcXHx4c/uGbwHlmH98tyvFfW4z2zHO+VdVx1v6wKJh9//LHZfXJycjB9+nQMHz4cI0eONLmvTqeDA1uSiIiIyMPY/amcmTNnomvXrpg4caLB9l27dqGoqAjl5eXYvn07jh07huTkZHteAhEREXkwuz6Vs3v3bpw+fRqpqalYt26dtF7s8Prf//4Xb7zxBoDKUfreffdd1K9f356XQERERB7MrsFk0KBBGDRokNHtljQFERERUfXFXkBERETkNhhMiIiIyG0wmBAREZHbcOjIr0RERETWYMWEiIiI3AaDCREREbkNBhMiIiJyGwwmRERE5DYYTIiIiMhtMJgQERGR22AwISIiIrfBYEJERERug8GEiIiI3AaDCRG5nevXr6Nz586uvgwicgGvDCalpaWYM2cOBg4ciJSUFEycOBEnTpyQtq9atQp9+vRBr1698P7770Mclf/y5ct49tln0adPH/Tu3RsvvPACMjIypPctWLAAQ4cORffu3TF27Fjs27fP6Z/NUQYPHoyuXbuiqKhIWldcXIzu3btj8ODBLrwy98N7VTWDBw/GsWPHXH0Zbu/XX3/FxIkTkZKSgt69e2PatGm4du2aqy/L7QwePBiDBg1CeXm5tG7u3LlYunSpC6/KfTjq+3Dp0qXSMYcPH45vv/3WbtfslcGkoqIC9erVw8cff4wff/wRDz30EJ599lkUFhZi//79WLt2LVatWoWvvvoKP//8s3RD8/Pz0bNnT2zYsAFbtmxBdHQ0Zs+eLR03JCQEixYtwu7du/H888/jtdde86p/KKKjo7F7927p9e7du1G7dm2rjyP/B8Jb2eteEWnJz8/Hc889h0ceeQQ//vgjvvvuOzz44IPw9fV19aW5pcLCQmzcuNHVl+GWHPV9+MADD2DdunXYs2cPFi5ciMWLF+P8+fN2uWavDCbBwcGYMmUKYmNj4ePjg/79+8Pf3x+pqanYvHkzhg8fjvr166N27doYP348Nm/eDABo06YNhgwZgpo1ayIgIABjxozB77//Lh136tSpiI+Ph4+PD5KSkpCQkIDTp0+76mPaXf/+/bFlyxbp9ZYtW3D//fdLrz/55BMMGjQIKSkpmDRpEs6dOydtGzx4MD799FOMGjUKw4cPd+p1u4Kt92rr1q144oknFMd6/fXX8cknnzjnwt3I7NmzsWLFCun1d999h+nTp7vwitxHamoqAgIC0KtXL/j4+CAkJAQ9e/ZEbGwsKioqsHTpUgwaNAj9+vXDggULpF8Gli5dildffRXPPfccunfvjilTpuD69esu/jSON27cOKxcuVLzl6K1a9di6NCh6NOnD1577TXk5+cDAKZPn47vv/9e2k+set68edNp1+0Mjvo+bNiwIYKDgwEAOp0OAOz2i7pXBhO1K1euIC8vDw0aNMClS5fQtGlTaVtiYiIuXLig+b7ffvsNCQkJmtvy8vJw4cIFo9s9UVJSEi5cuIDs7GxkZ2fj/PnzSE5OlrY3atQIn3/+OXbt2oXOnTvj9ddfV7z/xx9/xNKlS7F27VpnX7rT2XqvevTogTNnzuDWrVsAgJKSEuzevRv9+/d3yecg9xQfH4+ysjK8+eabOHjwoPRlCgCrV6/Gb7/9hs8//xzr16/H6dOnsX79emn7Dz/8gGHDhmHXrl1o06aNwd9Tb5SUlITY2Fh89913ivUHDx7EihUrsGDBAnz33XcoLi7Gu+++CwDo27cvduzYIe27b98+JCYmIjY21qnX7mz2/D5ctWoVunbtihEjRiA6Otpu/cK8PpgUFxfjtddew8SJExEWFobCwkKEhoZK20NDQxV9BURpaWn48MMPMWPGDINter0ec+bMQa9evdC4cWOHXr8z+fr6olevXti+fTu2b9+OXr16KUrHvXr1QkREBPz8/KQqQGFhobR97NixiIqKQlBQkCsu36lsvVdBQUFISUnB9u3bAQD79+9HkyZNEBcX56qPQm4oLCwMy5YtQ2lpKV5//XX07dsXr732GgoKCvDtt9/iySefREREBGrUqIHx48dj165d0nvvuusudOvWDf7+/pg6dSp+//13Rd8AbzVlyhSDqsn27dsxYsQIJCQkIDg4GDNmzMCOHTsgCAJ69uyJX3/9FXl5eQCAHTt2oF+/fq66fKew9/fhxIkTsW/fPqxatQq9evWCn5+fXa7TPkdxU+Xl5Xj55ZfRoEEDTJkyBUBlP5GCggJpn4KCAqkcJcrIyMDMmTMxbdo0dOrUyeC48+fPR35+PubNm+fYD+ACDzzwAN59910IgoDnn38eer1e2vb1119jzZo1SE9Ph06ngyAIyM3NRUhICAAgJibGVZftErbeqwEDBmDx4sUYP348tm3bxmoJaUpMTMSbb74JAPjzzz/x8ssv45NPPsHNmzfx9NNPS+VzQRAQHR0tvU/+9zAoKAi1atXC7du3UadOHed+ACdLTk5GnTp1FM0zt2/fxl133SW9rlu3LkpKSpCbm4vw8HB06NABP/74I/r27YsDBw7gxRdfdMWlO4Wjvg91Oh3atGmDzZs34+uvv8bo0aOrfK1eG0z0ej1ee+016HQ6zJ49W/pL3LhxY5w/fx4pKSkAgAsXLqBJkybS+3JycjB9+nQMHz4cI0eONDju+++/j9OnT2PJkiUICAhwzodxolatWiE3NxcA0Lp1a6lN8fr163jvvfewbNkytGjRAqWlpejWrZvUgxv4q52xurD1XnXq1Am3bt3C6dOncfDgQbzyyisu+wyuFBwcjJKSEul1ZmamC6/GvbVs2RI9e/bEhQsXEB0djXnz5qFFixaa+6anp0vLxcXFyM3NrTYds6dMmYJ58+bh7rvvBgDUrl1bcT9u3ryJwMBA1KpVCwDQr18/bNu2DYGBgWjZsqXX3idHfR/KVVRUIC0tzS7X67VNOXPnzkVmZibmz5+vKC8NGDAAGzZswNWrV5GZmYnVq1djwIABACp7Ic+cORNdu3bFxIkTDY65YsUK7N+/H4sWLVKUv7zNO++8g3feeUexrrCwED4+PoiIiJA635Ft98rX1xf9+vXDP//5T7Rv3x4RERHOvGS30bRpU/z000/Iz8/H1atX+VSFzOXLl7F69WqpCSY1NRV79+5F69atMWTIECxevBi3b9+GIAi4fv06jh49Kr33xIkT2L9/P8rKyrB8+XK0bt3a66slonvuuQdRUVHYs2cPgMrg8fXXX+PSpUsoKirC4sWL0adPH+mLuUePHjh27BjWr1/v1c04jvg+/Prrr3Hnzh3o9XocOXIEW7du1ayo2MIrKyY3btzAN998g8DAQPTp00dav2jRInTt2hWjRo3Co48+Cr1ej2HDhmHo0KEAKh/5PH36NFJTU7Fu3TrpfeJ4JR999BH8/f0VY1W8+uqreOCBB5z0yZxDq0NvYmIiRowYgbFjxyI4OBiTJ0+Gv7+/C67Ovdh6rwYMGID//ve/ePTRR511qW5nwIABOHDgAAYOHIhGjRqhf//+OH78uKsvyy2EhITgxIkT+Oyzz1BQUIBatWqhd+/emDhxInQ6HSoqKvD4448jJycHsbGxip+jXr164euvv8Yrr7yCZs2a4Y033nDhJ3G+KVOm4KmnngJQGVQmTpyIWbNmoaCgAPfccw+ee+45ad+wsDAkJydj//79eOutt1x1yQ7lqO/Dffv24YMPPkBZWRliY2Mxa9YsdOvWzS7XrBPktXgicors7GwMGTIE27Ztk/roVBe9e/fGxx9/jEaNGrn6UrzO0qVLcevWLbz22muuvhQim3ltUw6RuxIEAV9++SV69+5d7ULJkSNHIAgC6tat6+pLISI35ZVNOUTu7P7770doaCg++OADV1+KU/3rX//CwYMH8eqrryIwMNDVl0NEbopNOUREROQ22JRDREREboPBhIiIiNwGgwkRERG5DQYTIiIichsMJkREROQ2GEyIyKGOHDmCpKQkJCUl4fr1666+HCJycwwmRGQ3s2fPRlJSEp544glpXVhYGNq0aYM2bdp45cSXRGRfHGCNiByqRYsWWLVqlasvg4g8BAdYIyK7GDx4MG7cuGGw/qOPPsK0adMAABs3bkS9evUwe/ZsfP/996hbty6mTp2KJUuWID8/H0OGDMGMGTPw4YcfYuPGjQgLC8OkSZMwatQo6XgZGRlYvHgxDhw4gJycHMTExGDw4MGYOHGiYuZUIvJM/FtMRHbRvHlzFBUVIScnB6GhoWjcuDEA4PTp00bfc/v2bcyfPx+1a9dGQUEB1qxZg4MHD+LWrVsICwtDeno63n77bdx9991o3LgxcnJyMHHiRKSnp0vnuHjxIj766CNcu3YNr7/+urM+LhE5CPuYEJFdvPvuu+jatSuAypCyatUqrFq1Ci1atDD6nrKyMnzwwQfYsGEDYmJiAABpaWlYs2YN1q5di8DAQOj1ehw9ehQA8NVXXyE9PR1RUVH45ptvsGbNGmm6+u+//x5paWkO/pRE5GismBCRy9SsWRPt27cHAMTGxiI9PR1NmjRBvXr1AAARERG4efMmsrKyAACnTp0CAGRmZqJv376KYwmCgJMnT6JBgwbO+wBEZHcMJkTkMqGhodKyr6+vwTqdTgegMnSo3yc2FckFBQU54jKJyIkYTIjIbsRgUFxc7JDjt2rVCj/99BN8fX0xd+5cqbJSUFCAH3/8ET179nTIeYnIeRhMiMhuGjVqBAD4448/8OCDDyI4OBhTpkyx2/HHjBmDb7/9Frdu3cLIkSPRuHFjFBQUID09HeXl5Rg0aJDdzkVErsHOr0RkN0OGDEGvXr0QFhaGCxcu4OTJk9Dr9XY7fkREBFauXInBgwejVq1auHDhAkpKStChQwf87W9/s9t5iMh1OI4JERERuQ1WTIiIiMhtMJgQERGR22AwISIiIrfBYEJERERug8GEiIiI3AaDCREREbkNBhMiIiJyGwwmRERE5DYYTIiIiMhtMJgQERGR22AwISIiIrfx/wEYIS/aeWc0fAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2450,7 +2660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "id": "c9d57d37-6c6a-4d0f-9fc7-f34a95f0cd8f",
    "metadata": {},
    "outputs": [],
@@ -2464,29 +2674,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "id": "5798d402-abd5-4eb1-b8ee-fdf3523703fc",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10.vega-embed details,\n",
+       "  #altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-0f44be7337e74d3abd6eb7b3b45c0d10\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2025-01-06T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.6859550966426222}, {\"time\": \"2025-01-07T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.6453657479710495}, {\"time\": \"2025-01-08T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.7239767082624007}, {\"time\": \"2025-01-09T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.8599648453488175}, {\"time\": \"2025-01-10T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.2790047942575692}, {\"time\": \"2025-01-11T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.8475409316421809}, {\"time\": \"2025-01-12T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.5932578472094062}, {\"time\": \"2025-01-13T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.950729785794465}, {\"time\": \"2025-01-14T16:54:24.016\", \"variable\": \"col1\", \"value\": 0.8049061393608224}, {\"time\": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2506,7 +2776,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -2520,7 +2790,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/0_core/1-models/1.0-autoencoder.ipynb b/docs/user_guide/0_core/1-models/1.0-autoencoder.ipynb
index a874c49..889026d 100644
--- a/docs/user_guide/0_core/1-models/1.0-autoencoder.ipynb
+++ b/docs/user_guide/0_core/1-models/1.0-autoencoder.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "d518f399-6d44-4903-8190-5c50e377beba",
    "metadata": {
     "ExecuteTime": {
@@ -33,23 +33,13 @@
     "sys.path.insert(0, '../../../../src')\n",
     "\n",
     "from ontime.core.time_series.time_series import TimeSeries\n",
-    "from ontime.core.model.models.autoencoder import Autoencoder\n",
-    "from ontime.module.data.sliced_dataset import SlicedDataset\n",
+    "from ontime.core.modelling.registry.autoencoder import Autoencoder\n",
+    "from ontime.module.processing.pytorch.sliced_dataset import SlicedDataset\n",
     "import torch.utils\n",
-    "import darts\n",
-    "from colorutils import Color\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "2a8064d715e4c498"
-  },
   {
    "cell_type": "markdown",
    "id": "55eaef5a-2258-4bc9-86a7-3daf7c74d6f4",
@@ -61,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "id": "5adc3f0c-fdc0-4517-a36f-e1aed8daf0d8",
    "metadata": {
     "ExecuteTime": {
@@ -72,24 +62,84 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "component   Sunspots\nMonth               \n1749-01-01      58.0\n1749-02-01      62.6\n1749-03-01      70.0\n1749-04-01      55.7\n1749-05-01      85.0",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>component</th>\n      <th>Sunspots</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1749-01-01</th>\n      <td>58.0</td>\n    </tr>\n    <tr>\n      <th>1749-02-01</th>\n      <td>62.6</td>\n    </tr>\n    <tr>\n      <th>1749-03-01</th>\n      <td>70.0</td>\n    </tr>\n    <tr>\n      <th>1749-04-01</th>\n      <td>55.7</td>\n    </tr>\n    <tr>\n      <th>1749-05-01</th>\n      <td>85.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>component</th>\n",
+       "      <th>Sunspots</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Month</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1749-01-01</th>\n",
+       "      <td>58.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1749-02-01</th>\n",
+       "      <td>62.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1749-03-01</th>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1749-04-01</th>\n",
+       "      <td>55.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1749-05-01</th>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component   Sunspots\n",
+       "Month               \n",
+       "1749-01-01      58.0\n",
+       "1749-02-01      62.6\n",
+       "1749-03-01      70.0\n",
+       "1749-04-01      55.7\n",
+       "1749-05-01      85.0"
+      ]
      },
-     "execution_count": 2,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "text/plain": "<Figure size 2000x500 with 1 Axes>",
-      "image/png": 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"
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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from ontime.module.data.dataset import Dataset\n",
+    "from ontime.module.datasets.dataset import Dataset\n",
     "\n",
     "data = Dataset.SunspotsDataset.load()\n",
     "\n",
@@ -112,14 +162,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "a9a35dac95269562",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-02-28T08:36:03.127771800Z",
      "start_time": "2024-02-28T08:36:02.578733200Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
@@ -145,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "430568a8-dc95-4b5f-b0e2-d74d541a5999",
    "metadata": {
     "ExecuteTime": {
@@ -172,21 +222,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "id": "bd3fd37ab8819a6d",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-02-28T08:36:04.488715500Z",
      "start_time": "2024-02-28T08:36:04.327500700Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<ontime.module.data.sliced_dataset.SlicedDataset object at 0x7f930a7090f0>\n"
+      "<ontime.module.processing.pytorch.sliced_dataset.SlicedDataset object at 0x7fbc2759d2d0>\n"
      ]
     }
    ],
@@ -208,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "fc5d026b-50b9-49d7-a669-9624942ad112",
    "metadata": {
     "ExecuteTime": {
@@ -244,21 +294,21 @@
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "columns = train.columns.tolist()\n",
-    "input_dimensions = len(columns) * period #we give (period) samples at once, meaning the AE receives nb features * period data points at once\n",
-    "latent_dimensions = input_dimensions//6 # an arbitrary value, can be anything smaller that input_dimensions"
-   ],
+   "execution_count": 11,
+   "id": "349951dd4c9c81e9",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-02-28T08:36:09.135601500Z",
      "start_time": "2024-02-28T08:36:09.127095700Z"
-    }
+    },
+    "collapsed": false
    },
-   "id": "349951dd4c9c81e9",
-   "execution_count": 7
+   "outputs": [],
+   "source": [
+    "columns = train.columns.tolist()\n",
+    "input_dimensions = len(columns) * period #we give (period) samples at once, meaning the AE receives nb features * period data points at once\n",
+    "latent_dimensions = input_dimensions//6 # an arbitrary value, can be anything smaller that input_dimensions"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -270,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "baf5f3de-4fff-469b-a091-7a74e0310fa0",
    "metadata": {
     "ExecuteTime": {
@@ -287,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "9223fc6b-1253-45b4-9da9-956eb96825f1",
    "metadata": {
     "ExecuteTime": {
@@ -325,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "id": "1e46e2e4-0038-45c0-90d8-65dac75362e1",
    "metadata": {
     "ExecuteTime": {
@@ -350,10 +400,40 @@
     }
    ],
    "source": [
-    "autoencoder = Autoencoder.new_encoder_for_dataset(train, period)\n",
+    "autoencoder, period = Autoencoder.new_encoder_for_dataset(train, period)\n",
     "print(autoencoder)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "cdfb8383",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Autoencoder(\n",
+       "  (encoder): Encoder(\n",
+       "    (linear1): Linear(in_features=125, out_features=78, bias=True)\n",
+       "    (linear2): Linear(in_features=78, out_features=31, bias=True)\n",
+       "  )\n",
+       "  (decoder): Decoder(\n",
+       "    (linear1): Linear(in_features=31, out_features=78, bias=True)\n",
+       "    (linear2): Linear(in_features=78, out_features=125, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "autoencoder"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f78192c8-2a19-4ea6-9e80-7eb644e4bbbf",
@@ -365,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "id": "64c870e0-fdf1-4964-955b-fb1c641921e5",
    "metadata": {
     "ExecuteTime": {
@@ -373,214 +453,7 @@
      "start_time": "2024-02-28T08:36:15.476924600Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 1/100\n",
-      "1/1\n",
-      "epoch 2/100\n",
-      "1/1\n",
-      "epoch 3/100\n",
-      "1/1\n",
-      "epoch 4/100\n",
-      "1/1\n",
-      "epoch 5/100\n",
-      "1/1\n",
-      "epoch 6/100\n",
-      "1/1\n",
-      "epoch 7/100\n",
-      "1/1\n",
-      "epoch 8/100\n",
-      "1/1\n",
-      "epoch 9/100\n",
-      "1/1\n",
-      "epoch 10/100\n",
-      "1/1\n",
-      "epoch 11/100\n",
-      "1/1\n",
-      "epoch 12/100\n",
-      "1/1\n",
-      "epoch 13/100\n",
-      "1/1\n",
-      "epoch 14/100\n",
-      "1/1\n",
-      "epoch 15/100\n",
-      "1/1\n",
-      "epoch 16/100\n",
-      "1/1\n",
-      "epoch 17/100\n",
-      "1/1\n",
-      "epoch 18/100\n",
-      "1/1\n",
-      "epoch 19/100\n",
-      "1/1\n",
-      "epoch 20/100\n",
-      "1/1\n",
-      "epoch 21/100\n",
-      "1/1\n",
-      "epoch 22/100\n",
-      "1/1\n",
-      "epoch 23/100\n",
-      "1/1\n",
-      "epoch 24/100\n",
-      "1/1\n",
-      "epoch 25/100\n",
-      "1/1\n",
-      "epoch 26/100\n",
-      "1/1\n",
-      "epoch 27/100\n",
-      "1/1\n",
-      "epoch 28/100\n",
-      "1/1\n",
-      "epoch 29/100\n",
-      "1/1\n",
-      "epoch 30/100\n",
-      "1/1\n",
-      "epoch 31/100\n",
-      "1/1\n",
-      "epoch 32/100\n",
-      "1/1\n",
-      "epoch 33/100\n",
-      "1/1\n",
-      "epoch 34/100\n",
-      "1/1\n",
-      "epoch 35/100\n",
-      "1/1\n",
-      "epoch 36/100\n",
-      "1/1\n",
-      "epoch 37/100\n",
-      "1/1\n",
-      "epoch 38/100\n",
-      "1/1\n",
-      "epoch 39/100\n",
-      "1/1\n",
-      "epoch 40/100\n",
-      "1/1\n",
-      "epoch 41/100\n",
-      "1/1\n",
-      "epoch 42/100\n",
-      "1/1\n",
-      "epoch 43/100\n",
-      "1/1\n",
-      "epoch 44/100\n",
-      "1/1\n",
-      "epoch 45/100\n",
-      "1/1\n",
-      "epoch 46/100\n",
-      "1/1\n",
-      "epoch 47/100\n",
-      "1/1\n",
-      "epoch 48/100\n",
-      "1/1\n",
-      "epoch 49/100\n",
-      "1/1\n",
-      "epoch 50/100\n",
-      "1/1\n",
-      "epoch 51/100\n",
-      "1/1\n",
-      "epoch 52/100\n",
-      "1/1\n",
-      "epoch 53/100\n",
-      "1/1\n",
-      "epoch 54/100\n",
-      "1/1\n",
-      "epoch 55/100\n",
-      "1/1\n",
-      "epoch 56/100\n",
-      "1/1\n",
-      "epoch 57/100\n",
-      "1/1\n",
-      "epoch 58/100\n",
-      "1/1\n",
-      "epoch 59/100\n",
-      "1/1\n",
-      "epoch 60/100\n",
-      "1/1\n",
-      "epoch 61/100\n",
-      "1/1\n",
-      "epoch 62/100\n",
-      "1/1\n",
-      "epoch 63/100\n",
-      "1/1\n",
-      "epoch 64/100\n",
-      "1/1\n",
-      "epoch 65/100\n",
-      "1/1\n",
-      "epoch 66/100\n",
-      "1/1\n",
-      "epoch 67/100\n",
-      "1/1\n",
-      "epoch 68/100\n",
-      "1/1\n",
-      "epoch 69/100\n",
-      "1/1\n",
-      "epoch 70/100\n",
-      "1/1\n",
-      "epoch 71/100\n",
-      "1/1\n",
-      "epoch 72/100\n",
-      "1/1\n",
-      "epoch 73/100\n",
-      "1/1\n",
-      "epoch 74/100\n",
-      "1/1\n",
-      "epoch 75/100\n",
-      "1/1\n",
-      "epoch 76/100\n",
-      "1/1\n",
-      "epoch 77/100\n",
-      "1/1\n",
-      "epoch 78/100\n",
-      "1/1\n",
-      "epoch 79/100\n",
-      "1/1\n",
-      "epoch 80/100\n",
-      "1/1\n",
-      "epoch 81/100\n",
-      "1/1\n",
-      "epoch 82/100\n",
-      "1/1\n",
-      "epoch 83/100\n",
-      "1/1\n",
-      "epoch 84/100\n",
-      "1/1\n",
-      "epoch 85/100\n",
-      "1/1\n",
-      "epoch 86/100\n",
-      "1/1\n",
-      "epoch 87/100\n",
-      "1/1\n",
-      "epoch 88/100\n",
-      "1/1\n",
-      "epoch 89/100\n",
-      "1/1\n",
-      "epoch 90/100\n",
-      "1/1\n",
-      "epoch 91/100\n",
-      "1/1\n",
-      "epoch 92/100\n",
-      "1/1\n",
-      "epoch 93/100\n",
-      "1/1\n",
-      "epoch 94/100\n",
-      "1/1\n",
-      "epoch 95/100\n",
-      "1/1\n",
-      "epoch 96/100\n",
-      "1/1\n",
-      "epoch 97/100\n",
-      "1/1\n",
-      "epoch 98/100\n",
-      "1/1\n",
-      "epoch 99/100\n",
-      "1/1\n",
-      "epoch 100/100\n",
-      "1/1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "autoencoder.train(tdata, device, epochs = 100)"
    ]
@@ -595,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "id": "094ff656-7a87-4fbf-a794-e3c53a5f976a",
    "metadata": {
     "ExecuteTime": {
@@ -610,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "id": "3badc9d4-4273-4d37-951f-31af7f6a060b",
    "metadata": {
     "ExecuteTime": {
@@ -628,10 +501,75 @@
     },
     {
      "data": {
-      "text/plain": "component    Sunspots       loss\nMonth                           \n1925-04-01  49.177569  10.147755\n1925-05-01  51.526049  10.147755\n1925-06-01  53.193726  10.147755\n1925-07-01  63.394775  10.147755\n1925-08-01  62.593162  10.147755",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>component</th>\n      <th>Sunspots</th>\n      <th>loss</th>\n    </tr>\n    <tr>\n      <th>Month</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1925-04-01</th>\n      <td>49.177569</td>\n      <td>10.147755</td>\n    </tr>\n    <tr>\n      <th>1925-05-01</th>\n      <td>51.526049</td>\n      <td>10.147755</td>\n    </tr>\n    <tr>\n      <th>1925-06-01</th>\n      <td>53.193726</td>\n      <td>10.147755</td>\n    </tr>\n    <tr>\n      <th>1925-07-01</th>\n      <td>63.394775</td>\n      <td>10.147755</td>\n    </tr>\n    <tr>\n      <th>1925-08-01</th>\n      <td>62.593162</td>\n      <td>10.147755</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>component</th>\n",
+       "      <th>Sunspots</th>\n",
+       "      <th>loss</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1925-04-01</th>\n",
+       "      <td>46.331925</td>\n",
+       "      <td>10.793975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1925-05-01</th>\n",
+       "      <td>46.186287</td>\n",
+       "      <td>10.793975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1925-06-01</th>\n",
+       "      <td>52.945164</td>\n",
+       "      <td>10.793975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1925-07-01</th>\n",
+       "      <td>75.413064</td>\n",
+       "      <td>10.793975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1925-08-01</th>\n",
+       "      <td>57.793451</td>\n",
+       "      <td>10.793975</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component    Sunspots       loss\n",
+       "Month                           \n",
+       "1925-04-01  46.331925  10.793975\n",
+       "1925-05-01  46.186287  10.793975\n",
+       "1925-06-01  52.945164  10.793975\n",
+       "1925-07-01  75.413064  10.793975\n",
+       "1925-08-01  57.793451  10.793975"
+      ]
      },
-     "execution_count": 13,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -654,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "id": "351807f2-05dd-4a5a-b218-f0db93bca3f1",
    "metadata": {
     "ExecuteTime": {
@@ -665,8 +603,10 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 2000x500 with 1 Axes>",
-      "image/png": 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"
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -705,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 26,
    "id": "aaaed348-adbc-459a-830a-af9076f30c8f",
    "metadata": {
     "ExecuteTime": {
@@ -723,20 +663,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "id": "f2b76cd7a5c7c574",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-02-28T08:36:59.821822800Z",
      "start_time": "2024-02-28T08:36:59.407573100Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 2000x500 with 1 Axes>",
-      "image/png": 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"
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",
+      "text/plain": [
+       "<Figure size 2000x500 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -769,7 +711,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/user_guide/1_module/0-data/1.0-data-dataset.ipynb b/docs/user_guide/1_module/0-data/1.0-data-dataset.ipynb
index c4f2eec..c6def74 100644
--- a/docs/user_guide/1_module/0-data/1.0-data-dataset.ipynb
+++ b/docs/user_guide/1_module/0-data/1.0-data-dataset.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "bad32b27-2cc2-4655-8059-d8c7d14323a2",
    "metadata": {},
    "outputs": [],
@@ -22,19 +22,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "7f4b4cf8-e3de-4409-a33e-3735ba67957f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from ontime.module.datasets.dataset import Dataset"
    ]
@@ -50,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "6e5d6c1d-040e-4f9a-a994-846b4643a1a1",
    "metadata": {},
    "outputs": [
@@ -58,12 +49,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<ontime.module.datasets.dataset.Dataset.DartsDataset object at 0x2c70d5f90>\n"
+      "<ontime.module.datasets.dataset.Dataset.DartsDataset object at 0x7fa2fcda3e50>\n"
      ]
     }
    ],
    "source": [
-    "ds = Dataset.AirPassengersDataset \n",
+    "ds = Dataset.GasRateCO2Dataset \n",
     "print(ds)"
    ]
   },
@@ -77,29 +68,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "id": "15d5230a-ca36-4ca1-b4f2-d2fe47055057",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-e8cbd5fc188a483384c1746bfc88c09d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-e8cbd5fc188a483384c1746bfc88c09d.vega-embed details,\n",
+       "  #altair-viz-e8cbd5fc188a483384c1746bfc88c09d.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-e8cbd5fc188a483384c1746bfc88c09d\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-e8cbd5fc188a483384c1746bfc88c09d\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-e8cbd5fc188a483384c1746bfc88c09d\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"variable\": \"GasRate(ft3/min)\", \"value\": -0.109, \"time\": \"1900-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.0, \"time\": \"1901-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.178, \"time\": \"1902-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.339, \"time\": \"1903-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.373, \"time\": \"1904-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.441, \"time\": \"1905-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.461, \"time\": \"1906-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.348, \"time\": \"1907-01-01T00:00:00.000\"}, {\"variable\": \"GasRate(ft3/min)\", \"value\": 0.127, \"time\": \"1908-01-01T00:00:00.000\"}, {\"variable\": 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+       "</script>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='Month'>"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -109,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "id": "d53a5170-5ca9-455f-bb48-9d3d5da13d59",
    "metadata": {},
    "outputs": [
@@ -117,122 +168,111 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<ontime.module.datasets.dataset.Dataset.OpenMLDataset object at 0x2c70d67d0>\n"
+      "<ontime.module.datasets.dataset.Dataset.DartsDataset object at 0x7fa2fcda3d60>\n"
      ]
     }
    ],
    "source": [
-    "ds2 = Dataset.AMDStockPrices \n",
+    "ds2 = Dataset.HeartRateDataset \n",
     "print(ds2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "id": "3bafa241-e7e1-4c5b-926a-78d9987b5456",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-1c5908cd137a4dd5a9bf097105de0259.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-1c5908cd137a4dd5a9bf097105de0259.vega-embed details,\n",
+       "  #altair-viz-1c5908cd137a4dd5a9bf097105de0259.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-1c5908cd137a4dd5a9bf097105de0259\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-1c5908cd137a4dd5a9bf097105de0259\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-1c5908cd137a4dd5a9bf097105de0259\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"variable\": \"Heart rate\", \"value\": 84.2697, \"time\": \"1900-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 84.2697, \"time\": \"1901-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 84.0619, \"time\": \"1902-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 85.6542, \"time\": \"1903-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 87.2093, \"time\": \"1904-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 87.1246, \"time\": \"1905-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 86.8726, \"time\": \"1906-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 86.7052, \"time\": \"1907-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 87.5899, \"time\": \"1908-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 89.1475, \"time\": \"1909-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 89.8204, \"time\": \"1910-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 89.8204, \"time\": \"1911-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 90.4375, \"time\": \"1912-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 91.7605, \"time\": \"1913-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 93.1081, \"time\": \"1914-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 94.3291, \"time\": \"1915-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.8003, \"time\": \"1916-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 97.5119, \"time\": \"1917-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 98.7457, \"time\": \"1918-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 98.904, \"time\": \"1919-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 98.3437, 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\"time\": \"1931-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 94.9367, \"time\": \"1932-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.0867, \"time\": \"1933-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.389, \"time\": \"1934-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.5414, \"time\": \"1935-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.2439, \"time\": \"1936-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 94.9415, \"time\": \"1937-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.3557, \"time\": \"1938-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 96.3423, \"time\": \"1939-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 97.1563, \"time\": \"1940-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 97.4026, \"time\": \"1941-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 96.7028, 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\"time\": \"1953-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.7692, \"time\": \"1954-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.9211, \"time\": \"1955-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 95.8501, \"time\": \"1956-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 94.6703, \"time\": \"1957-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 93.0993, \"time\": \"1958-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 91.972, \"time\": \"1959-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 91.7821, \"time\": \"1960-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 91.7911, \"time\": \"1961-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 90.807, \"time\": \"1962-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 89.3196, \"time\": \"1963-01-01T00:00:00.000\"}, {\"variable\": \"Heart rate\", \"value\": 88.1511, 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+       "</script>"
+      ],
       "text/plain": [
-       "<Axes: xlabel='Date'>"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
     "ts2 = ds2.load() #loaded as a TimeSeries\n",
     "ts2.plot()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ffa09008-e30c-4fb2-88c3-b1c744d22b4d",
-   "metadata": {},
-   "source": [
-    "Est-ce que c'est la bonne signature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb9cf00f-4e0e-4ea2-82d3-93f8a6fea87a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Benchmark(my_model_list, my_dataset_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f18330b4-e77f-4650-8423-d11885469df5",
-   "metadata": {},
-   "source": [
-    "DatasetList, est-ce qu'on en a vraiment besoin ou est-ce qu'une list serait suffisante?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "13d97bc2-0664-4e5f-8bea-7d3534ad43d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'MyDatasetList' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mMyDatasetList\u001b[49m()\u001b[38;5;241m.\u001b[39madd_dataset([\n\u001b[1;32m      2\u001b[0m     Dataset\u001b[38;5;241m.\u001b[39mAMDStockPrices,\n\u001b[1;32m      3\u001b[0m ])\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'MyDatasetList' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "MyDatasetList().add_dataset([\n",
-    "    Dataset.AMDStockPrices,\n",
-    "    TimeSeries\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "443a0764-bb79-4f77-a296-12ec4af19840",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Dataset.load(my_dataset)\n",
-    "Dataset.my_dataset\n",
-    "Dataset.list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a302451-2866-4b18-ba76-1547bdce5585",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -246,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/user_guide/1_module/0.6-anomaly-frequency.ipynb b/docs/user_guide/1_module/0.6-anomaly-frequency.ipynb
index a360f29..e1aa864 100644
--- a/docs/user_guide/1_module/0.6-anomaly-frequency.ipynb
+++ b/docs/user_guide/1_module/0.6-anomaly-frequency.ipynb
@@ -41,16 +41,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import to be able to import python package from src\n",
     "import sys\n",
@@ -112,7 +103,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -153,7 +144,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -170,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "5025638542c13b76",
    "metadata": {
     "ExecuteTime": {
@@ -193,7 +184,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 600x400 with 2 Axes>"
       ]
@@ -208,15 +199,15 @@
     "ts_detect = det.detect(ts)\n",
     "\n",
     "fig, ax = plt.subplots(2, figsize=(6, 4))\n",
-    "ts.plot(ax=ax[0], label='ts')\n",
-    "ts_detect.plot(ax=ax[1], label='anomalies')\n",
+    "ts.pd_series().plot(ax=ax[0], label='ts')\n",
+    "ts_detect.pd_series().plot(ax=ax[1], label='anomalies')\n",
     "fig.subplots_adjust(hspace=.5)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "c651dabda3a3fbff",
    "metadata": {
     "ExecuteTime": {
@@ -243,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "619b3f0b4dde4cb6",
    "metadata": {
     "ExecuteTime": {
@@ -266,23 +257,83 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-8489995062c145018f1b795da06d74f8.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-8489995062c145018f1b795da06d74f8.vega-embed details,\n",
+       "  #altair-viz-8489995062c145018f1b795da06d74f8.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-8489995062c145018f1b795da06d74f8\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-8489995062c145018f1b795da06d74f8\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-8489995062c145018f1b795da06d74f8\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-10T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -293,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "3b538017c065be80",
    "metadata": {
     "ExecuteTime": {
@@ -316,23 +367,83 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-46dafe3f6b9b4dee882dbff3682d03ef.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-46dafe3f6b9b4dee882dbff3682d03ef.vega-embed details,\n",
+       "  #altair-viz-46dafe3f6b9b4dee882dbff3682d03ef.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-46dafe3f6b9b4dee882dbff3682d03ef\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-46dafe3f6b9b4dee882dbff3682d03ef\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-46dafe3f6b9b4dee882dbff3682d03ef\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2022-01-01T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-02T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-03T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-04T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-05T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-06T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-07T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-08T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-09T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-10T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-11T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-12T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-13T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-14T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-15T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-16T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-17T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-18T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-19T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-20T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": \"2022-01-21T00:00:00.000\", \"variable\": \"random_walk\", \"value\": 0.0}, {\"time\": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -352,7 +463,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -366,7 +477,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/1_module/0.7-anomaly-time-series-plot.ipynb b/docs/user_guide/1_module/0.7-anomaly-time-series-plot.ipynb
deleted file mode 100644
index e5a5121..0000000
--- a/docs/user_guide/1_module/0.7-anomaly-time-series-plot.ipynb
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ab811dec64537290",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.005612,
-     "end_time": "2024-01-31T17:50:17.694924",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:17.689312",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "# Example of anomaly plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "initial_id",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.303204600Z",
-     "start_time": "2023-11-23T12:01:00.068161300Z"
-    },
-    "papermill": {
-     "duration": 2.607726,
-     "end_time": "2024-01-31T17:50:20.308212",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:17.700486",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import to be able to import python package from src\n",
-    "import sys\n",
-    "\n",
-    "sys.path.insert(0, '../../src')\n",
-    "import pandas as pd\n",
-    "import ontime as on\n",
-    "from ontime.core.plotting.anomaly_plot import AnomalyPlot\n",
-    "from ontime.core.time_series import BinaryTimeSeries, TimeSeries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ab697b35b8f5f65",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.001264,
-     "end_time": "2024-01-31T17:50:20.310902",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.309638",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "---\n",
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "eec107566af4e692",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.331721100Z",
-     "start_time": "2023-11-23T12:01:05.310731700Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.00786,
-     "end_time": "2024-01-31T17:50:20.320031",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.312171",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "data_ts = on.generators.random_walk().generate(start=pd.Timestamp('2021-01-01'), end=pd.Timestamp('2022-12-31'))\n",
-    "data_df = data_ts.pd_dataframe()\n",
-    "data_df.rename(columns={'random_walk': 'data'}, inplace=True)\n",
-    "data_ts = TimeSeries.from_dataframe(data_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "647bf1cab9416df8",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.001189,
-     "end_time": "2024-01-31T17:50:20.322429",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.321240",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "---\n",
-    "## Creating mock anomalies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d637aa322628a1b2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.359268500Z",
-     "start_time": "2023-11-23T12:01:05.322958900Z"
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-    "collapsed": false,
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-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.323560",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Assuming 'anomalies' is a new column in data_df indicating anomalies with values 0 or 1\n",
-    "import numpy as np\n",
-    "\n",
-    "np.random.seed(42)  # Setting seed for reproducibility\n",
-    "anomalies_df = pd.DataFrame({'anomalies': np.random.choice([0, 1], size=len(data_df), p=[0.99, 0.01])},\n",
-    "                            index=data_df.index)\n",
-    "\n",
-    "anomalies_ts = BinaryTimeSeries.from_dataframe(anomalies_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "43742a19bc26e3d8",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.359268500Z",
-     "start_time": "2023-11-23T12:01:05.343839600Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.006786,
-     "end_time": "2024-01-31T17:50:20.341212",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.334426",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "subset_data_df = anomalies_df.iloc[140:160]\n",
-    "subset_data_df.loc[:, 'anomalies'] = 1\n",
-    "subset_data_ts = BinaryTimeSeries.from_dataframe(subset_data_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a80e292f0e234acd",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.411098600Z",
-     "start_time": "2023-11-23T12:01:05.354269Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.007402,
-     "end_time": "2024-01-31T17:50:20.349803",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.342401",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "collective_anomalies_df = anomalies_df.copy()  # Create a copy of anomalies_df\n",
-    "\n",
-    "# Initialize the 'anomalies' column in the copy to 0\n",
-    "collective_anomalies_df['anomalies'] = 0\n",
-    "\n",
-    "# Set the values to 1 where the specified conditions on the index are met\n",
-    "collective_anomalies_df.loc[(\n",
-    "        ((collective_anomalies_df.index >= '2021-05-21') & (collective_anomalies_df.index <= '2021-05-31')) |\n",
-    "        ((collective_anomalies_df.index >= '2021-06-21') & (collective_anomalies_df.index <= '2021-06-30')) |\n",
-    "        ((collective_anomalies_df.index >= '2021-07-21') & (collective_anomalies_df.index <= '2021-07-31'))\n",
-    "), 'anomalies'] = 1\n",
-    "\n",
-    "collective_an_ts = BinaryTimeSeries.from_dataframe(collective_anomalies_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84cf149443600a9",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "papermill": {
-     "duration": 0.001223,
-     "end_time": "2024-01-31T17:50:20.352254",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.351031",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "---\n",
-    "## Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dffe76c6afad027b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-23T12:01:05.579865800Z",
-     "start_time": "2023-11-23T12:01:05.366115200Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
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-    "papermill": {
-     "duration": 0.096669,
-     "end_time": "2024-01-31T17:50:20.450073",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.353404",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
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\"2021-06-03T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -6.9629390169872325}, {\"time\": \"2021-06-04T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -7.186993502224946}, {\"time\": \"2021-06-05T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -7.584724502232168}, {\"time\": \"2021-06-06T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -9.21374954973685}, {\"time\": \"2021-06-07T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -8.491686556569885}, {\"time\": \"2021-06-08T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -8.360202948550729}, {\"time\": \"2021-06-09T00:00:00\", \"anomalies\": 1.0, \"anomalies_y\": -8.131737444525756}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.LayerChart(...)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chart = AnomalyPlot.plot_anomalies(data_ts,\n",
-    "                                   point_anomalies=anomalies_ts,\n",
-    "                                   contextual_anomalies=subset_data_ts,\n",
-    "                                   collective_anomalies=collective_an_ts\n",
-    "                                   )\n",
-    "chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a8dce1a-bd0a-43a2-a287-db61d091b9d4",
-   "metadata": {
-    "papermill": {
-     "duration": 0.001577,
-     "end_time": "2024-01-31T17:50:20.453463",
-     "exception": false,
-     "start_time": "2024-01-31T17:50:20.451886",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  },
-  "papermill": {
-   "default_parameters": {},
-   "duration": 4.096641,
-   "end_time": "2024-01-31T17:50:21.075732",
-   "environment_variables": {},
-   "exception": null,
-   "input_path": "docs/user_guide/0_core/0.7-anomaly-time-series-plot.ipynb",
-   "output_path": "docs/user_guide/0_core/0.7-anomaly-time-series-plot.ipynb",
-   "parameters": {},
-   "start_time": "2024-01-31T17:50:16.979091",
-   "version": "2.5.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/user_guide/1_module/1-processing/1.0-preprocessing-common.ipynb b/docs/user_guide/1_module/1-processing/1.0-preprocessing-common.ipynb
index 0e7bd73..197c2af 100644
--- a/docs/user_guide/1_module/1-processing/1.0-preprocessing-common.ipynb
+++ b/docs/user_guide/1_module/1-processing/1.0-preprocessing-common.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe",
    "metadata": {
     "papermill": {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f",
    "metadata": {
     "papermill": {
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "4733b4e6-71a2-42b2-93fd-a5615b84ac1a",
    "metadata": {
     "papermill": {
@@ -73,16 +73,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import ontime as on"
    ]
@@ -107,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "e9a96d79-0423-4d79-b01d-726193216238",
    "metadata": {
     "papermill": {
@@ -161,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "id": "a630af5c-687e-48e2-a6d4-5a8cb1d1ec66",
    "metadata": {
     "papermill": {
@@ -175,7 +166,7 @@
    },
    "outputs": [],
    "source": [
-    "from ontime.module import preprocessing"
+    "from ontime.module import processing"
    ]
   },
   {
@@ -197,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "id": "a4b12f07-8a97-403a-a554-89e166574120",
    "metadata": {
     "papermill": {
@@ -214,15 +205,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:480: RuntimeWarning: All-NaN slice encountered\n",
-      "  data_min = np.nanmin(X, axis=0)\n",
-      "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:481: RuntimeWarning: All-NaN slice encountered\n",
-      "  data_max = np.nanmax(X, axis=0)\n"
+      "/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/sklearn/utils/_array_api.py:472: RuntimeWarning: All-NaN slice encountered\n",
+      "  return xp.asarray(numpy.nanmin(X, axis=axis))\n",
+      "/home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/sklearn/utils/_array_api.py:489: RuntimeWarning: All-NaN slice encountered\n",
+      "  return xp.asarray(numpy.nanmax(X, axis=axis))\n"
      ]
     }
    ],
    "source": [
-    "ts_t = preprocessing.common.normalize(ts)"
+    "ts_t = processing.common.normalize(ts)"
    ]
   },
   {
@@ -244,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "id": "8b67892d-db8c-4f12-93b6-147016da4186",
    "metadata": {
     "papermill": {
@@ -258,7 +249,7 @@
    },
    "outputs": [],
    "source": [
-    "train, test = preprocessing.common.train_test_split(ts_t, train_split=0.8)"
+    "train, test = processing.common.train_test_split(ts_t, train_split=0.8)"
    ]
   },
   {
@@ -275,12 +266,12 @@
     "tags": []
    },
    "source": [
-    "### Split time series in chunks"
+    "### Split time series in windows"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "id": "500e954a-82d6-4eff-bbdd-0b889c2a10f8",
    "metadata": {
     "papermill": {
@@ -294,8 +285,8 @@
    },
    "outputs": [],
    "source": [
-    "train_list = preprocessing.common.split_by_length(train, 6)\n",
-    "test_list = preprocessing.common.split_by_length(test, 6)"
+    "train_list = processing.common.split_in_windows(train, 100, 100)\n",
+    "test_list = processing.common.split_in_windows(test, 100, 100)"
    ]
   },
   {
@@ -317,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "id": "f7897c44-71ba-4752-86c6-547387245ae4",
    "metadata": {
     "execution": {
@@ -337,8 +328,8 @@
    },
    "outputs": [],
    "source": [
-    "X_train, y_train = preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n",
-    "X_test, y_test = preprocessing.common.split_inputs_from_targets(test_list, 4, 2)"
+    "X_train, y_train = processing.common.split_inputs_from_targets(train_list, 70, 30)\n",
+    "X_test, y_test = processing.common.split_inputs_from_targets(test_list, 70, 30)"
    ]
   },
   {
@@ -360,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "id": "a4ab9cfa-289d-4d8e-be40-d5d4247f5ab5",
    "metadata": {
     "execution": {
@@ -380,15 +371,15 @@
    },
    "outputs": [],
    "source": [
-    "X_train = preprocessing.common.timeseries_list_to_numpy(X_train)\n",
-    "y_train = preprocessing.common.timeseries_list_to_numpy(y_train)\n",
-    "X_test = preprocessing.common.timeseries_list_to_numpy(X_test)\n",
-    "y_test = preprocessing.common.timeseries_list_to_numpy(y_test)"
+    "X_train = processing.common.timeseries_list_to_numpy(X_train)\n",
+    "y_train = processing.common.timeseries_list_to_numpy(y_train)\n",
+    "X_test = processing.common.timeseries_list_to_numpy(X_test)\n",
+    "y_test = processing.common.timeseries_list_to_numpy(y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "id": "1b0a2843-6d02-4b08-96f8-91712e521bf5",
    "metadata": {
     "execution": {
@@ -411,10 +402,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(4675, 4, 28)\n",
-      "(4675, 2, 28)\n",
-      "(1168, 4, 28)\n",
-      "(1168, 2, 28)\n"
+      "(280, 28, 70)\n",
+      "(280, 28, 30)\n",
+      "(70, 28, 70)\n",
+      "(70, 28, 30)\n"
      ]
     }
    ],
@@ -428,7 +419,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -442,7 +433,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/1_module/1-processing/pytorch/1.0_pytorch-dataset.ipynb b/docs/user_guide/1_module/1-processing/pytorch/1.0_pytorch-dataset.ipynb
index eaab5a6..0e89a58 100644
--- a/docs/user_guide/1_module/1-processing/pytorch/1.0_pytorch-dataset.ipynb
+++ b/docs/user_guide/1_module/1-processing/pytorch/1.0_pytorch-dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "70a32352-80c9-40b7-8f68-1aeecfc52658",
    "metadata": {
     "ExecuteTime": {
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "f8a26d78-229f-47f7-9f66-d0c245dbc096",
    "metadata": {
     "ExecuteTime": {
@@ -52,10 +52,8 @@
     "import numpy as np\n",
     "\n",
     "import tensorflow as tf\n",
-    "from tensorflow.data import Dataset\n",
     "\n",
     "from ontime.module.processing.pytorch.utils import create_dataset\n",
-    "from ontime.module.processing.common import split_in_windows, split_inputs_from_targets, timeseries_list_to_numpy\n",
     "from ontime.module.processing.common import train_test_split, normalize"
    ]
   },
@@ -77,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "e6f47f17-3c04-4cc9-b806-26b37baa3030",
    "metadata": {},
    "outputs": [],
@@ -102,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "d39b1594-2104-410f-980b-654130379d13",
    "metadata": {},
    "outputs": [],
@@ -122,14 +120,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "7f9f2ed4-a4b3-4312-821d-6e2b09776731",
    "metadata": {},
    "outputs": [],
    "source": [
     "ds_train = create_dataset(\n",
     "    train_enc,\n",
-    "    window_length = 10,\n",
     "    stride_length = 10,\n",
     "    input_length = 6,      \n",
     "    target_length = 4\n",
@@ -137,7 +134,6 @@
     "\n",
     "ds_test = create_dataset(\n",
     "    test_enc,\n",
-    "    window_length = 10,\n",
     "    stride_length = 10,\n",
     "    input_length = 6,      \n",
     "    target_length = 4\n",
@@ -162,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "id": "e1ebc46a-c318-4add-b808-27168434b853",
    "metadata": {},
    "outputs": [],
@@ -181,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 8,
    "id": "d12152ce-0370-4ef7-9a9e-ff4dd8f552e2",
    "metadata": {},
    "outputs": [
@@ -218,13 +214,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -269,7 +266,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -277,7 +274,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -289,6 +288,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -310,7 +313,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -321,7 +324,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -393,15 +396,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -551,7 +554,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 6, component: 2, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 6, component: 2, sample: 1)&gt; Size: 96B\n",
        "array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
@@ -570,12 +573,12 @@
        "       [[  5.],\n",
        "        [105.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3 4 5\n",
-       "  * component  (component) &lt;U1 &#x27;0&#x27; &#x27;1&#x27;\n",
+       "  * time       (time) int64 48B 0 1 2 3 4 5\n",
+       "  * component  (component) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-733e507e-2893-48e3-90a7-db39e3250c19' class='xr-array-in' type='checkbox' checked><label for='section-733e507e-2893-48e3-90a7-db39e3250c19' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 100.0 1.0 101.0 2.0 102.0 3.0 103.0 4.0 104.0 5.0 105.0</span></div><div class='xr-array-data'><pre>array([[[  0.],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5a635d77-93b9-4f31-8cfc-9c20b96f542c' class='xr-array-in' type='checkbox' checked><label for='section-5a635d77-93b9-4f31-8cfc-9c20b96f542c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 100.0 1.0 101.0 2.0 102.0 3.0 103.0 4.0 104.0 5.0 105.0</span></div><div class='xr-array-data'><pre>array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
        "       [[  1.],\n",
@@ -591,10 +594,10 @@
        "        [104.]],\n",
        "\n",
        "       [[  5.],\n",
-       "        [105.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-3a604d0f-5208-442d-8ead-7710d9bb21c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a604d0f-5208-442d-8ead-7710d9bb21c9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-6a59d911-7668-4923-8965-b73b57c617c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a59d911-7668-4923-8965-b73b57c617c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bcb4191-8897-4c21-9e14-945b3ae1a9b9' class='xr-var-data-in' type='checkbox'><label for='data-5bcb4191-8897-4c21-9e14-945b3ae1a9b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-a697a798-d2b9-481e-b205-10aa6273103e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a697a798-d2b9-481e-b205-10aa6273103e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d307facd-458f-453d-90e7-fbb04e6a6028' class='xr-var-data-in' type='checkbox'><label for='data-d307facd-458f-453d-90e7-fbb04e6a6028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-faed968d-7dc8-4561-a590-044fc61f2e39' class='xr-section-summary-in' type='checkbox'  ><label for='section-faed968d-7dc8-4561-a590-044fc61f2e39' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01abb8c1-2782-4fac-bc4f-30e238e00832' class='xr-index-data-in' type='checkbox'/><label for='index-01abb8c1-2782-4fac-bc4f-30e238e00832' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=6, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-59a09166-a44d-4bb2-a8aa-43f6b3b2b833' class='xr-index-data-in' type='checkbox'/><label for='index-59a09166-a44d-4bb2-a8aa-43f6b3b2b833' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6647c06b-a28c-4cb3-8d03-a57cab359bdc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6647c06b-a28c-4cb3-8d03-a57cab359bdc' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "        [105.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-95dc14e6-8d24-4c7b-92b1-eb9662f6cf75' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95dc14e6-8d24-4c7b-92b1-eb9662f6cf75' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-653a7847-90b5-4a7b-bc7d-112adee38d89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-653a7847-90b5-4a7b-bc7d-112adee38d89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcfad59a-82c5-4483-8f16-2d30a4c93e5c' class='xr-var-data-in' type='checkbox'><label for='data-dcfad59a-82c5-4483-8f16-2d30a4c93e5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-55e65cf1-e10f-4e17-88bb-9077cf110cc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55e65cf1-e10f-4e17-88bb-9077cf110cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54890d6b-df30-4bca-9e23-8c869d65a821' class='xr-var-data-in' type='checkbox'><label for='data-54890d6b-df30-4bca-9e23-8c869d65a821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2bd5bb1a-49f0-4da5-ba90-12a484d47e42' class='xr-section-summary-in' type='checkbox'  ><label for='section-2bd5bb1a-49f0-4da5-ba90-12a484d47e42' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cc4cbcd5-7610-49bb-80dd-24c6d80f1938' class='xr-index-data-in' type='checkbox'/><label for='index-cc4cbcd5-7610-49bb-80dd-24c6d80f1938' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=6, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-05ca8516-6d21-4fe2-bb1d-2173ac73137e' class='xr-index-data-in' type='checkbox'/><label for='index-05ca8516-6d21-4fe2-bb1d-2173ac73137e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a8abef2-bdd6-44f6-b21a-834b9c453367' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a8abef2-bdd6-44f6-b21a-834b9c453367' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 6, component: 2, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 6, component: 2, sample: 1)> Size: 96B\n",
        "array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
@@ -613,15 +616,15 @@
        "       [[  5.],\n",
        "        [105.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3 4 5\n",
-       "  * component  (component) <U1 '0' '1'\n",
+       "  * time       (time) int64 48B 0 1 2 3 4 5\n",
+       "  * component  (component) <U1 8B '0' '1'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -640,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 9,
    "id": "cd34444f-d501-4905-82af-ce6a5f83c63b",
    "metadata": {},
    "outputs": [
@@ -677,13 +680,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -728,7 +732,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -736,7 +740,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -748,6 +754,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -769,7 +779,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -780,7 +790,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -852,15 +862,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1010,7 +1020,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 4, component: 2, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 4, component: 2, sample: 1)&gt; Size: 64B\n",
        "array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
@@ -1023,12 +1033,12 @@
        "       [[  9.],\n",
        "        [109.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3\n",
-       "  * component  (component) &lt;U1 &#x27;0&#x27; &#x27;1&#x27;\n",
+       "  * time       (time) int64 32B 0 1 2 3\n",
+       "  * component  (component) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6f008556-b22e-47dc-b805-1e8447e575df' class='xr-array-in' type='checkbox' checked><label for='section-6f008556-b22e-47dc-b805-1e8447e575df' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.0 106.0 7.0 107.0 8.0 108.0 9.0 109.0</span></div><div class='xr-array-data'><pre>array([[[  6.],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f1ec47a2-e790-4a48-8788-2ec1fdb195b9' class='xr-array-in' type='checkbox' checked><label for='section-f1ec47a2-e790-4a48-8788-2ec1fdb195b9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.0 106.0 7.0 107.0 8.0 108.0 9.0 109.0</span></div><div class='xr-array-data'><pre>array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
        "       [[  7.],\n",
@@ -1038,10 +1048,10 @@
        "        [108.]],\n",
        "\n",
        "       [[  9.],\n",
-       "        [109.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-b17df813-362c-47a1-9916-5142af00efd2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b17df813-362c-47a1-9916-5142af00efd2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-aff7f659-dbcd-4dab-931b-918deea8e36a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aff7f659-dbcd-4dab-931b-918deea8e36a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab96bdaf-bba0-4ecf-bbb0-6bb160865e15' class='xr-var-data-in' type='checkbox'><label for='data-ab96bdaf-bba0-4ecf-bbb0-6bb160865e15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-533f5db8-bdb5-4407-8564-19456a4bf334' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-533f5db8-bdb5-4407-8564-19456a4bf334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43df4cdf-8757-4800-8a23-042097d9c083' class='xr-var-data-in' type='checkbox'><label for='data-43df4cdf-8757-4800-8a23-042097d9c083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68345071-398b-4f8f-be24-6b5f71ac7dba' class='xr-section-summary-in' type='checkbox'  ><label for='section-68345071-398b-4f8f-be24-6b5f71ac7dba' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c7d277c-5924-4bcd-b690-05176ca739f5' class='xr-index-data-in' type='checkbox'/><label for='index-1c7d277c-5924-4bcd-b690-05176ca739f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=4, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1eb1e124-c949-4060-a1d4-e219f5b71d0c' class='xr-index-data-in' type='checkbox'/><label for='index-1eb1e124-c949-4060-a1d4-e219f5b71d0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15c44b44-8cd1-4c5a-a3cf-b80886c14b4b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15c44b44-8cd1-4c5a-a3cf-b80886c14b4b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "        [109.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-18042652-6f18-4f8e-881f-80faa9ddb469' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18042652-6f18-4f8e-881f-80faa9ddb469' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0b435160-f2f1-4967-b529-589090b1248e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b435160-f2f1-4967-b529-589090b1248e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-002a1d6c-785a-413d-9706-d9d64a8f3687' class='xr-var-data-in' type='checkbox'><label for='data-002a1d6c-785a-413d-9706-d9d64a8f3687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-7f8248ba-c226-4b3d-a882-5674465d345e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f8248ba-c226-4b3d-a882-5674465d345e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32ce8a31-f529-4bd0-95a7-0f33d2d6eca6' class='xr-var-data-in' type='checkbox'><label for='data-32ce8a31-f529-4bd0-95a7-0f33d2d6eca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1fdb573f-00da-4687-9b5a-594011db386c' class='xr-section-summary-in' type='checkbox'  ><label for='section-1fdb573f-00da-4687-9b5a-594011db386c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d8e580b0-2cdd-4085-ab0d-a630f02a82b4' class='xr-index-data-in' type='checkbox'/><label for='index-d8e580b0-2cdd-4085-ab0d-a630f02a82b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=4, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7781e3c7-995a-4179-b534-15cd08f3b3a7' class='xr-index-data-in' type='checkbox'/><label for='index-7781e3c7-995a-4179-b534-15cd08f3b3a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff86b86c-b43d-43fc-bb97-c515a69c6641' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff86b86c-b43d-43fc-bb97-c515a69c6641' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 4, component: 2, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 4, component: 2, sample: 1)> Size: 64B\n",
        "array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
@@ -1054,15 +1064,15 @@
        "       [[  9.],\n",
        "        [109.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3\n",
-       "  * component  (component) <U1 '0' '1'\n",
+       "  * time       (time) int64 32B 0 1 2 3\n",
+       "  * component  (component) <U1 8B '0' '1'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1082,7 +1092,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1096,7 +1106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/1_module/1-processing/tensorflow/1.0_tensorflow-dataset.ipynb b/docs/user_guide/1_module/1-processing/tensorflow/1.0_tensorflow-dataset.ipynb
index c6c1239..dafafff 100644
--- a/docs/user_guide/1_module/1-processing/tensorflow/1.0_tensorflow-dataset.ipynb
+++ b/docs/user_guide/1_module/1-processing/tensorflow/1.0_tensorflow-dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 1,
    "id": "70a32352-80c9-40b7-8f68-1aeecfc52658",
    "metadata": {
     "ExecuteTime": {
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 3,
    "id": "f8a26d78-229f-47f7-9f66-d0c245dbc096",
    "metadata": {
     "ExecuteTime": {
@@ -45,17 +45,11 @@
    },
    "outputs": [],
    "source": [
-    "from datetime import datetime\n",
-    "\n",
     "import ontime as on\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.data import Dataset\n",
-    "\n",
     "from ontime.module.processing.tensorflow.utils import create_dataset\n",
-    "from ontime.module.processing.common import split_in_windows, split_inputs_from_targets, timeseries_list_to_numpy\n",
     "from ontime.module.processing.common import train_test_split, normalize"
    ]
   },
@@ -77,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 4,
    "id": "e6f47f17-3c04-4cc9-b806-26b37baa3030",
    "metadata": {},
    "outputs": [],
@@ -102,12 +96,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 6,
    "id": "d39b1594-2104-410f-980b-654130379d13",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = train_test_split(ts_multi, test_split=0.3)\n",
+    "train, test = train_test_split(ts, test_split=0.3)\n",
     "train_enc, transformer = normalize(train, return_transformer=True)\n",
     "test_enc = transformer.transform(test)"
    ]
@@ -122,10 +116,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 7,
    "id": "7f9f2ed4-a4b3-4312-821d-6e2b09776731",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "I0000 00:00:1736180054.254876  319935 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5564 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3070 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
    "source": [
     "ds_train = create_dataset(\n",
     "    train_enc,\n",
@@ -162,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 8,
    "id": "6bd1446b-1c4b-48a7-92e1-d0a5f8338b65",
    "metadata": {},
    "outputs": [
@@ -170,7 +172,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-18 14:02:59.461476: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
+      "2025-01-06 17:14:44.385253: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
      ]
     }
    ],
@@ -194,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 9,
    "id": "d12152ce-0370-4ef7-9a9e-ff4dd8f552e2",
    "metadata": {},
    "outputs": [
@@ -231,13 +233,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -282,7 +285,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -290,7 +293,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -302,6 +307,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -323,7 +332,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -334,7 +343,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -406,15 +415,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -564,7 +573,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 6, component: 2, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 6, component: 2, sample: 1)&gt; Size: 96B\n",
        "array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
@@ -583,12 +592,12 @@
        "       [[  5.],\n",
        "        [105.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3 4 5\n",
-       "  * component  (component) &lt;U1 &#x27;0&#x27; &#x27;1&#x27;\n",
+       "  * time       (time) int64 48B 0 1 2 3 4 5\n",
+       "  * component  (component) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b9f9879d-8629-459a-883d-41ce38241b7d' class='xr-array-in' type='checkbox' checked><label for='section-b9f9879d-8629-459a-883d-41ce38241b7d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 100.0 1.0 101.0 2.0 102.0 3.0 103.0 4.0 104.0 5.0 105.0</span></div><div class='xr-array-data'><pre>array([[[  0.],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fe6063c7-4dc8-42c7-a1de-e230d576bc01' class='xr-array-in' type='checkbox' checked><label for='section-fe6063c7-4dc8-42c7-a1de-e230d576bc01' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 100.0 1.0 101.0 2.0 102.0 3.0 103.0 4.0 104.0 5.0 105.0</span></div><div class='xr-array-data'><pre>array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
        "       [[  1.],\n",
@@ -604,10 +613,10 @@
        "        [104.]],\n",
        "\n",
        "       [[  5.],\n",
-       "        [105.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8f6d8b8f-7569-43a1-81da-98838053478d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f6d8b8f-7569-43a1-81da-98838053478d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-7969d141-27a4-4ef8-bd00-b06b394af573' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7969d141-27a4-4ef8-bd00-b06b394af573' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58d85e67-a0dd-4e5a-9955-db6603b22245' class='xr-var-data-in' type='checkbox'><label for='data-58d85e67-a0dd-4e5a-9955-db6603b22245' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-40d2adf0-99e5-459b-85a3-99cf004cdfc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40d2adf0-99e5-459b-85a3-99cf004cdfc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-492db351-ff26-4855-a1e9-4756b3224cea' class='xr-var-data-in' type='checkbox'><label for='data-492db351-ff26-4855-a1e9-4756b3224cea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2597e04b-71d3-4b31-b823-5208860a0668' class='xr-section-summary-in' type='checkbox'  ><label for='section-2597e04b-71d3-4b31-b823-5208860a0668' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eb138ed2-ab56-454f-ae8f-54b0d0816302' class='xr-index-data-in' type='checkbox'/><label for='index-eb138ed2-ab56-454f-ae8f-54b0d0816302' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=6, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53376f29-27c3-4693-854b-0600f24e7b49' class='xr-index-data-in' type='checkbox'/><label for='index-53376f29-27c3-4693-854b-0600f24e7b49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4bf7f0ab-0a98-4bea-9e06-601406ef7bf9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4bf7f0ab-0a98-4bea-9e06-601406ef7bf9' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "        [105.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-0b96b8d0-5662-4545-9673-1483e52f369a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b96b8d0-5662-4545-9673-1483e52f369a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-fd1a7079-2e8b-46c6-9ac6-a6554ed3f6b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd1a7079-2e8b-46c6-9ac6-a6554ed3f6b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0d90c3-f160-4a7c-a7dd-c2251f39c6ec' class='xr-var-data-in' type='checkbox'><label for='data-8d0d90c3-f160-4a7c-a7dd-c2251f39c6ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-ef0a45e8-0e45-4140-a01e-d3a4b2b6dcab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef0a45e8-0e45-4140-a01e-d3a4b2b6dcab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f65fa110-ce59-4a7c-914d-51a70a3e7e60' class='xr-var-data-in' type='checkbox'><label for='data-f65fa110-ce59-4a7c-914d-51a70a3e7e60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-184b95f7-9085-46ab-a139-7b55df16c5bd' class='xr-section-summary-in' type='checkbox'  ><label for='section-184b95f7-9085-46ab-a139-7b55df16c5bd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a16bf4f0-3e5b-4265-ae58-351a37b6d4c6' class='xr-index-data-in' type='checkbox'/><label for='index-a16bf4f0-3e5b-4265-ae58-351a37b6d4c6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=6, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1c90bce9-1152-40f5-b7c9-9ccbfdb7c75f' class='xr-index-data-in' type='checkbox'/><label for='index-1c90bce9-1152-40f5-b7c9-9ccbfdb7c75f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdd26028-54b2-4eba-a60c-b0359b4386a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdd26028-54b2-4eba-a60c-b0359b4386a6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 6, component: 2, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 6, component: 2, sample: 1)> Size: 96B\n",
        "array([[[  0.],\n",
        "        [100.]],\n",
        "\n",
@@ -626,15 +635,15 @@
        "       [[  5.],\n",
        "        [105.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3 4 5\n",
-       "  * component  (component) <U1 '0' '1'\n",
+       "  * time       (time) int64 48B 0 1 2 3 4 5\n",
+       "  * component  (component) <U1 8B '0' '1'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -653,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 10,
    "id": "cd34444f-d501-4905-82af-ce6a5f83c63b",
    "metadata": {},
    "outputs": [
@@ -690,13 +699,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -741,7 +751,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -749,7 +759,9 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -761,6 +773,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -782,7 +798,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -793,7 +809,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -865,15 +881,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -1023,7 +1039,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 4, component: 2, sample: 1)&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 4, component: 2, sample: 1)&gt; Size: 64B\n",
        "array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
@@ -1036,12 +1052,12 @@
        "       [[  9.],\n",
        "        [109.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3\n",
-       "  * component  (component) &lt;U1 &#x27;0&#x27; &#x27;1&#x27;\n",
+       "  * time       (time) int64 32B 0 1 2 3\n",
+       "  * component  (component) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-deb33e39-3d3b-4221-949a-c2ec74e454e5' class='xr-array-in' type='checkbox' checked><label for='section-deb33e39-3d3b-4221-949a-c2ec74e454e5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.0 106.0 7.0 107.0 8.0 108.0 9.0 109.0</span></div><div class='xr-array-data'><pre>array([[[  6.],\n",
+       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>component</span>: 2</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-64143d71-1177-47ad-9d25-28f81fded4f5' class='xr-array-in' type='checkbox' checked><label for='section-64143d71-1177-47ad-9d25-28f81fded4f5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.0 106.0 7.0 107.0 8.0 108.0 9.0 109.0</span></div><div class='xr-array-data'><pre>array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
        "       [[  7.],\n",
@@ -1051,10 +1067,10 @@
        "        [108.]],\n",
        "\n",
        "       [[  9.],\n",
-       "        [109.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ff1788d5-4631-43ae-824a-4770402b0f42' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff1788d5-4631-43ae-824a-4770402b0f42' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-86b5bc6e-3ca7-4e01-a1f2-4432ae012361' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86b5bc6e-3ca7-4e01-a1f2-4432ae012361' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5e9bde3-ef2a-4775-bf2d-87c0b137ea7d' class='xr-var-data-in' type='checkbox'><label for='data-a5e9bde3-ef2a-4775-bf2d-87c0b137ea7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-db34608e-d12a-4e69-9180-19df2f11cb90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db34608e-d12a-4e69-9180-19df2f11cb90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96318ee2-2f9c-47e6-9fe5-622aaa6c2e6d' class='xr-var-data-in' type='checkbox'><label for='data-96318ee2-2f9c-47e6-9fe5-622aaa6c2e6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3cfd7780-5445-45a5-82a8-2206e20877dd' class='xr-section-summary-in' type='checkbox'  ><label for='section-3cfd7780-5445-45a5-82a8-2206e20877dd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e6067b7-d6a3-4f71-a0be-4cf4b7531f94' class='xr-index-data-in' type='checkbox'/><label for='index-0e6067b7-d6a3-4f71-a0be-4cf4b7531f94' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=4, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5342d61-fd46-4044-9e16-c2a24b740a9a' class='xr-index-data-in' type='checkbox'/><label for='index-a5342d61-fd46-4044-9e16-c2a24b740a9a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae15198c-d133-4624-b092-d4e17158d658' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae15198c-d133-4624-b092-d4e17158d658' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
+       "        [109.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-959eb87e-7e73-4363-9a77-3d9b9cb3ea10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-959eb87e-7e73-4363-9a77-3d9b9cb3ea10' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-29171895-68de-4b31-9312-98573d0214b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29171895-68de-4b31-9312-98573d0214b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60accfd5-a855-488b-a0a3-013028322dfb' class='xr-var-data-in' type='checkbox'><label for='data-60accfd5-a855-488b-a0a3-013028322dfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-2d9dd575-4a13-46b7-82be-5ec34331410b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d9dd575-4a13-46b7-82be-5ec34331410b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8baac5ac-2c07-416f-83e1-3fe49d7c9f3a' class='xr-var-data-in' type='checkbox'><label for='data-8baac5ac-2c07-416f-83e1-3fe49d7c9f3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bf2d77c-a680-480c-aeed-1cf84ebca77b' class='xr-section-summary-in' type='checkbox'  ><label for='section-8bf2d77c-a680-480c-aeed-1cf84ebca77b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-456643c5-4735-4e18-8f25-72faca1e094d' class='xr-index-data-in' type='checkbox'/><label for='index-456643c5-4735-4e18-8f25-72faca1e094d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=4, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-32315cd9-ebba-41df-b777-8492c8e7866f' class='xr-index-data-in' type='checkbox'/><label for='index-32315cd9-ebba-41df-b777-8492c8e7866f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-968de770-cb8e-434a-81ef-e0a53efc0e16' class='xr-section-summary-in' type='checkbox'  checked><label for='section-968de770-cb8e-434a-81ef-e0a53efc0e16' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<TimeSeries (DataArray) (time: 4, component: 2, sample: 1)>\n",
+       "<TimeSeries (DataArray) (time: 4, component: 2, sample: 1)> Size: 64B\n",
        "array([[[  6.],\n",
        "        [106.]],\n",
        "\n",
@@ -1067,15 +1083,15 @@
        "       [[  9.],\n",
        "        [109.]]])\n",
        "Coordinates:\n",
-       "  * time       (time) int64 0 1 2 3\n",
-       "  * component  (component) <U1 '0' '1'\n",
+       "  * time       (time) int64 32B 0 1 2 3\n",
+       "  * component  (component) <U1 8B '0' '1'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
        "    hierarchy:          None"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1095,7 +1111,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1109,7 +1125,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/docs/user_guide/1_module/3-benchmarking/3.0-benchmarking.ipynb b/docs/user_guide/1_module/3-benchmarking/3.0-benchmarking.ipynb
index 56a618c..7bce1c9 100644
--- a/docs/user_guide/1_module/3-benchmarking/3.0-benchmarking.ipynb
+++ b/docs/user_guide/1_module/3-benchmarking/3.0-benchmarking.ipynb
@@ -95,7 +95,7 @@
     "\n",
     "# torch related parameters\n",
     "pl_trainer_kwargs = {\n",
-    "    \"accelerator\": \"gpu\",\n",
+    "    \"accelerator\": \"cpu\",\n",
     "    \"enable_progress_bar\": False\n",
     "    }\n",
     "\n",
@@ -187,7 +187,7 @@
    },
    "outputs": [],
    "source": [
-    "benchmark.add_dataset(BenchmarkDataset(Dataset.ETTh1Dataset.load(), input_length=336, gap=0, stride=72, target_length=72, name = \"ETTh1\"))"
+    "benchmark.add_dataset(BenchmarkDataset(Dataset.ETTh1Dataset.load()[:5000], input_length=336, gap=0, stride=72, target_length=72, name = \"ETTh1\"))"
    ]
   },
   {
@@ -220,7 +220,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 0: 11:44:37 - [INFO] - BenchmarkLogger - On Daily temperature dataset...\n"
+      "on 0: 08:34:31 - [INFO] - BenchmarkLogger - On Daily temperature dataset...\n"
      ]
     },
     {
@@ -234,7 +234,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 1: 11:44:37 - [INFO] - BenchmarkLogger - ExponentialSmoothing model...\n"
+      "on 1: 08:34:31 - [INFO] - BenchmarkLogger - ExponentialSmoothing model...\n"
      ]
     },
     {
@@ -248,17 +248,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 1: 11:44:37 - [INFO] - BenchmarkLogger - Evaluating...\n"
+      "on 1: 08:34:31 - [INFO] - BenchmarkLogger - Evaluating...\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "on 1: False                                                                                                                                                                                             \n",
-      "on 1: MAE                                                                                                                                                                                               \n",
-      "on 1: sMAPE                                                                                                                                                                                             \n",
-      "on 1: MASE                                                                                                                                                                                              \n",
       "                                                                                                                                                                                                        \r"
      ]
     },
@@ -266,7 +262,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 1: 11:44:38 - [INFO] - BenchmarkLogger - Evaluation done, took 1.0009534358978271\n"
+      "on 1: 08:34:31 - [INFO] - BenchmarkLogger - Evaluation done, took 0.530205249786377\n"
      ]
     },
     {
@@ -280,7 +276,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 1: 11:44:38 - [INFO] - BenchmarkLogger - getting predictions...\n"
+      "on 1: 08:34:31 - [INFO] - BenchmarkLogger - getting predictions...\n"
      ]
     },
     {
@@ -294,7 +290,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 1: 11:44:38 - [INFO] - BenchmarkLogger - Computed metrics: \n",
+      "on 1: 08:34:31 - [INFO] - BenchmarkLogger - Computed metrics: \n",
       "       {'MAE': 2.400040758215776, 'sMAPE': 24.148347520589876, 'MASE': 1.259049717016714}\n"
      ]
     },
@@ -309,7 +305,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: 11:44:38 - [INFO] - BenchmarkLogger - Temporal Convolutional Network model...\n"
+      "on 2: 08:34:31 - [INFO] - BenchmarkLogger - Temporal Convolutional Network model...\n"
      ]
     },
     {
@@ -330,7 +326,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "on 2: True                                                                                                                                                                                              \n",
       "                                                                                                                                                                                                        \r"
      ]
     },
@@ -380,7 +375,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: INFO: GPU available: True (cuda), used: True\n"
+      "on 2: INFO: GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -394,7 +389,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -464,49 +459,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: INFO: You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 2: lightning.pytorch.utilities.rank_zero INFO  You are using a CUDA device ('NVIDIA GeForce RTX 3070 Ti Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 2: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 2: \n",
+      "on 2: /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+      "      \n",
       "        | Name            | Type             | Params | Mode \n",
       "      -------------------------------------------------------------\n",
       "      0 | criterion       | MSELoss          | 0      | train\n",
@@ -563,7 +517,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: 11:44:46 - [INFO] - BenchmarkLogger - Training done, it took 7.525358438491821 seconds\n"
+      "on 2: 08:34:36 - [INFO] - BenchmarkLogger - Training done, it took 4.2949488162994385 seconds\n"
      ]
     },
     {
@@ -577,7 +531,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: 11:44:46 - [INFO] - BenchmarkLogger - Evaluating...\n"
+      "on 2: 08:34:36 - [INFO] - BenchmarkLogger - Evaluating...\n"
      ]
     },
     {
@@ -591,7 +545,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: INFO: GPU available: True (cuda), used: True\n"
+      "on 2: INFO: GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -605,7 +559,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -675,24 +629,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "on 2: MAE                                                                                                                                                                                               \n",
-      "on 2: sMAPE                                                                                                                                                                                             \n",
-      "on 2: MASE                                                                                                                                                                                              \n",
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 2: 11:44:46 - [INFO] - BenchmarkLogger - Evaluation done, took 0.5738580226898193\n"
+      "on 2: 08:34:36 - [INFO] - BenchmarkLogger - Evaluation done, took 0.5235123634338379\n"
      ]
     },
     {
@@ -706,7 +643,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: 11:44:46 - [INFO] - BenchmarkLogger - getting predictions...\n"
+      "on 2: 08:34:36 - [INFO] - BenchmarkLogger - getting predictions...\n"
      ]
     },
     {
@@ -720,7 +657,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: INFO: GPU available: True (cuda), used: True\n"
+      "on 2: INFO: GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -734,7 +671,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 2: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -804,22 +741,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 2: 11:44:47 - [INFO] - BenchmarkLogger - Computed metrics: \n",
-      "       {'MAE': 2.348732890574183, 'sMAPE': 23.846048763201882, 'MASE': 1.200195414642612}\n"
+      "on 2: 08:34:37 - [INFO] - BenchmarkLogger - Computed metrics: \n",
+      "       {'MAE': 2.441540289454, 'sMAPE': 24.977305752818385, 'MASE': 1.2361577158238866}\n"
      ]
     },
     {
@@ -833,7 +756,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 2: 11:44:47 - [INFO] - BenchmarkLogger - On ETTh1 dataset...\n"
+      "on 2: 08:34:37 - [INFO] - BenchmarkLogger - On ETTh1 dataset...\n"
      ]
     },
     {
@@ -847,7 +770,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 3: 11:44:47 - [INFO] - BenchmarkLogger - ExponentialSmoothing model...\n"
+      "on 3: 08:34:37 - [INFO] - BenchmarkLogger - ExponentialSmoothing model...\n"
      ]
     },
     {
@@ -861,17 +784,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 3: 11:44:47 - [INFO] - BenchmarkLogger - Evaluating...\n"
+      "on 3: 08:34:37 - [INFO] - BenchmarkLogger - Evaluating...\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "on 3: False                                                                                                                                                                                             \n",
-      "on 3: MAE                                                                                                                                                                                               \n",
-      "on 3: sMAPE                                                                                                                                                                                             \n",
-      "on 3: MASE                                                                                                                                                                                              \n",
       "                                                                                                                                                                                                        \r"
      ]
     },
@@ -879,7 +798,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 3: 11:45:48 - [INFO] - BenchmarkLogger - Evaluation done, took 61.609753370285034\n"
+      "on 3: 08:34:48 - [INFO] - BenchmarkLogger - Evaluation done, took 11.291918516159058\n"
      ]
     },
     {
@@ -893,7 +812,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 3: 11:45:48 - [INFO] - BenchmarkLogger - getting predictions...\n"
+      "on 3: 08:34:48 - [INFO] - BenchmarkLogger - getting predictions...\n"
      ]
     },
     {
@@ -907,8 +826,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 3: 11:45:50 - [INFO] - BenchmarkLogger - Computed metrics: \n",
-      "       {'MAE': 3.518272782469046, 'sMAPE': 62.05659914168031, 'MASE': 2.9669407195966673}\n"
+      "on 3: 08:34:49 - [INFO] - BenchmarkLogger - Computed metrics: \n",
+      "       {'MAE': 1.6361909709419278, 'sMAPE': 54.35492758310376, 'MASE': 2.4054684211651254}\n"
      ]
     },
     {
@@ -922,7 +841,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: 11:45:50 - [INFO] - BenchmarkLogger - Temporal Convolutional Network model...\n"
+      "on 4: 08:34:49 - [INFO] - BenchmarkLogger - Temporal Convolutional Network model...\n"
      ]
     },
     {
@@ -943,7 +862,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "on 4: True                                                                                                                                                                                              \n",
       "                                                                                                                                                                                                        \r"
      ]
     },
@@ -951,7 +869,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: darts.models.forecasting.torch_forecasting_model INFO  Train dataset contains 10740 samples.\n"
+      "on 4: darts.models.forecasting.torch_forecasting_model INFO  Train dataset contains 2791 samples.\n"
      ]
     },
     {
@@ -993,7 +911,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: INFO: GPU available: True (cuda), used: True\n"
+      "on 4: INFO: GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -1007,7 +925,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -1077,21 +995,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 4: \n",
+      "on 4: /home/benjy/.cache/pypoetry/virtualenvs/ontime-2OQVvbNf-py3.10/lib/python3.10/site-packages/pytorch_lightning/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n",
+      "      \n",
       "        | Name            | Type             | Params | Mode \n",
       "      -------------------------------------------------------------\n",
       "      0 | criterion       | MSELoss          | 0      | train\n",
@@ -1148,7 +1053,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: 11:46:13 - [INFO] - BenchmarkLogger - Training done, it took 23.201418161392212 seconds\n"
+      "on 4: 08:34:58 - [INFO] - BenchmarkLogger - Training done, it took 9.150264978408813 seconds\n"
      ]
     },
     {
@@ -1162,7 +1067,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: 11:46:13 - [INFO] - BenchmarkLogger - Evaluating...\n"
+      "on 4: 08:34:58 - [INFO] - BenchmarkLogger - Evaluating...\n"
      ]
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     {
@@ -1176,7 +1081,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: INFO: GPU available: True (cuda), used: True\n"
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@@ -1190,7 +1095,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
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     {
@@ -1260,24 +1165,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "on 4: MAE                                                                                                                                                                                               \n",
-      "on 4: sMAPE                                                                                                                                                                                             \n",
-      "on 4: MASE                                                                                                                                                                                              \n",
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 4: 11:46:14 - [INFO] - BenchmarkLogger - Evaluation done, took 1.4504737854003906\n"
+      "on 4: 08:34:59 - [INFO] - BenchmarkLogger - Evaluation done, took 0.6921942234039307\n"
      ]
     },
     {
@@ -1291,7 +1179,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: 11:46:14 - [INFO] - BenchmarkLogger - getting predictions...\n"
+      "on 4: 08:34:59 - [INFO] - BenchmarkLogger - getting predictions...\n"
      ]
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     {
@@ -1305,7 +1193,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: INFO: GPU available: True (cuda), used: True\n"
+      "on 4: INFO: GPU available: True (cuda), used: False\n"
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     {
@@ -1319,7 +1207,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: True\n"
+      "on 4: lightning.pytorch.utilities.rank_zero INFO  GPU available: True (cuda), used: False\n"
      ]
     },
     {
@@ -1389,29 +1277,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "on 4: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+      "on 4: 08:34:59 - [INFO] - BenchmarkLogger - Computed metrics: \n",
+      "       {'MAE': 2.529447460260427, 'sMAPE': 99.82058944577555, 'MASE': 4.438016277794369}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "on 4: 11:46:15 - [INFO] - BenchmarkLogger - Computed metrics: \n",
-      "       {'MAE': 2.867906589673361, 'sMAPE': 84.92323548841348, 'MASE': 4.189970881462188}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Benchmarking |████████████████████| 4/4 [100%] in 1:38.5 (0.04/s)                                                                                                                                       \n"
+      "Benchmarking |████████████████████| 4/4 [100%] in 28.9s (0.14/s)                                                                                                                                        \n"
      ]
     }
    ],
@@ -1464,38 +1338,38 @@
       "ExponentialSmoothing model:\n",
       "suceeded: ✓\n",
       "training time: 0\n",
-      "evaluation time: 1.0009534358978271\n",
-      "inference time: 0.12173032760620117\n",
+      "evaluation time: 0.530205249786377\n",
+      "inference time: 0.06768155097961426\n",
       "metrics: {'MAE': 2.400040758215776, 'sMAPE': 24.148347520589876, 'MASE': 1.259049717016714}\n",
       "\n",
       "Temporal Convolutional Network model:\n",
       "suceeded: ✓\n",
-      "training time: 7.525358438491821\n",
-      "evaluation time: 0.5738580226898193\n",
-      "inference time: 0.4079856872558594\n",
-      "metrics: {'MAE': 2.348732890574183, 'sMAPE': 23.846048763201882, 'MASE': 1.200195414642612}\n",
+      "training time: 4.2949488162994385\n",
+      "evaluation time: 0.5235123634338379\n",
+      "inference time: 0.3829374313354492\n",
+      "metrics: {'MAE': 2.441540289454, 'sMAPE': 24.977305752818385, 'MASE': 1.2361577158238866}\n",
       "\n",
       "\n",
       "ETTh1 dataset:\n",
       "nb features: 7\n",
       "target column: ['HUFL', 'HULL', 'MUFL', 'MULL', 'LUFL', 'LULL', 'OT']\n",
-      "training set size: 11147\n",
-      "validation set size: 2788\n",
-      "test set size: 3485\n",
+      "training set size: 3198\n",
+      "validation set size: 801\n",
+      "test set size: 1001\n",
       "\n",
       "ExponentialSmoothing model:\n",
       "suceeded: ✓\n",
       "training time: 0\n",
-      "evaluation time: 61.609753370285034\n",
-      "inference time: 1.4529154300689697\n",
-      "metrics: {'MAE': 3.518272782469046, 'sMAPE': 62.05659914168031, 'MASE': 2.9669407195966673}\n",
+      "evaluation time: 11.291918516159058\n",
+      "inference time: 1.0528616905212402\n",
+      "metrics: {'MAE': 1.6361909709419278, 'sMAPE': 54.35492758310376, 'MASE': 2.4054684211651254}\n",
       "\n",
       "Temporal Convolutional Network model:\n",
       "suceeded: ✓\n",
-      "training time: 23.201418161392212\n",
-      "evaluation time: 1.4504737854003906\n",
-      "inference time: 0.4251244068145752\n",
-      "metrics: {'MAE': 2.867906589673361, 'sMAPE': 84.92323548841348, 'MASE': 4.189970881462188}\n",
+      "training time: 9.150264978408813\n",
+      "evaluation time: 0.6921942234039307\n",
+      "inference time: 0.3425025939941406\n",
+      "metrics: {'MAE': 2.529447460260427, 'sMAPE': 99.82058944577555, 'MASE': 4.438016277794369}\n",
       "\n"
      ]
     }
@@ -1514,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "687a0b79-ff02-4bde-aff7-6d6ccd5e7d40",
    "metadata": {
     "ExecuteTime": {
@@ -1567,17 +1441,17 @@
        "    <tr>\n",
        "      <th>training set size</th>\n",
        "      <td>2335</td>\n",
-       "      <td>11147</td>\n",
+       "      <td>3198</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>validation set size</th>\n",
        "      <td>585</td>\n",
-       "      <td>2788</td>\n",
+       "      <td>801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>test set size</th>\n",
        "      <td>732</td>\n",
-       "      <td>3485</td>\n",
+       "      <td>1001</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1596,12 +1470,12 @@
        "Characteristic                                                 \n",
        "nb features                                                 7  \n",
        "target column        [HUFL, HULL, MUFL, MULL, LUFL, LULL, OT]  \n",
-       "training set size                                       11147  \n",
-       "validation set size                                      2788  \n",
-       "test set size                                            3485  "
+       "training set size                                        3198  \n",
+       "validation set size                                       801  \n",
+       "test set size                                            1001  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1613,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "a113a1a8-6988-4440-9afc-c4e9b5e6f898",
    "metadata": {},
    "outputs": [
@@ -1654,63 +1528,63 @@
        "      <th rowspan=\"6\" valign=\"top\">Daily temperature</th>\n",
        "      <th>training time</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>7.525358</td>\n",
+       "      <td>4.294949</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>evaluation time</th>\n",
-       "      <td>1.000953</td>\n",
-       "      <td>0.573858</td>\n",
+       "      <td>0.530205</td>\n",
+       "      <td>0.523512</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>inference time</th>\n",
-       "      <td>0.121730</td>\n",
-       "      <td>0.407986</td>\n",
+       "      <td>0.067682</td>\n",
+       "      <td>0.382937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAE</th>\n",
        "      <td>2.400041</td>\n",
-       "      <td>2.348733</td>\n",
+       "      <td>2.441540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sMAPE</th>\n",
        "      <td>24.148348</td>\n",
-       "      <td>23.846049</td>\n",
+       "      <td>24.977306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MASE</th>\n",
        "      <td>1.259050</td>\n",
-       "      <td>1.200195</td>\n",
+       "      <td>1.236158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">ETTh1</th>\n",
        "      <th>training time</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>23.201418</td>\n",
+       "      <td>9.150265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>evaluation time</th>\n",
-       "      <td>61.609753</td>\n",
-       "      <td>1.450474</td>\n",
+       "      <td>11.291919</td>\n",
+       "      <td>0.692194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>inference time</th>\n",
-       "      <td>1.452915</td>\n",
-       "      <td>0.425124</td>\n",
+       "      <td>1.052862</td>\n",
+       "      <td>0.342503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAE</th>\n",
-       "      <td>3.518273</td>\n",
-       "      <td>2.867907</td>\n",
+       "      <td>1.636191</td>\n",
+       "      <td>2.529447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sMAPE</th>\n",
-       "      <td>62.056599</td>\n",
-       "      <td>84.923235</td>\n",
+       "      <td>54.354928</td>\n",
+       "      <td>99.820589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MASE</th>\n",
-       "      <td>2.966941</td>\n",
-       "      <td>4.189971</td>\n",
+       "      <td>2.405468</td>\n",
+       "      <td>4.438016</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1720,35 +1594,35 @@
        "                                   ExponentialSmoothing  \\\n",
        "Dataset           Metric                                  \n",
        "Daily temperature training time                0.000000   \n",
-       "                  evaluation time              1.000953   \n",
-       "                  inference time               0.121730   \n",
+       "                  evaluation time              0.530205   \n",
+       "                  inference time               0.067682   \n",
        "                  MAE                          2.400041   \n",
        "                  sMAPE                       24.148348   \n",
        "                  MASE                         1.259050   \n",
        "ETTh1             training time                0.000000   \n",
-       "                  evaluation time             61.609753   \n",
-       "                  inference time               1.452915   \n",
-       "                  MAE                          3.518273   \n",
-       "                  sMAPE                       62.056599   \n",
-       "                  MASE                         2.966941   \n",
+       "                  evaluation time             11.291919   \n",
+       "                  inference time               1.052862   \n",
+       "                  MAE                          1.636191   \n",
+       "                  sMAPE                       54.354928   \n",
+       "                  MASE                         2.405468   \n",
        "\n",
        "                                   Temporal Convolutional Network  \n",
        "Dataset           Metric                                           \n",
-       "Daily temperature training time                          7.525358  \n",
-       "                  evaluation time                        0.573858  \n",
-       "                  inference time                         0.407986  \n",
-       "                  MAE                                    2.348733  \n",
-       "                  sMAPE                                 23.846049  \n",
-       "                  MASE                                   1.200195  \n",
-       "ETTh1             training time                         23.201418  \n",
-       "                  evaluation time                        1.450474  \n",
-       "                  inference time                         0.425124  \n",
-       "                  MAE                                    2.867907  \n",
-       "                  sMAPE                                 84.923235  \n",
-       "                  MASE                                   4.189971  "
+       "Daily temperature training time                          4.294949  \n",
+       "                  evaluation time                        0.523512  \n",
+       "                  inference time                         0.382937  \n",
+       "                  MAE                                    2.441540  \n",
+       "                  sMAPE                                 24.977306  \n",
+       "                  MASE                                   1.236158  \n",
+       "ETTh1             training time                          9.150265  \n",
+       "                  evaluation time                        0.692194  \n",
+       "                  inference time                         0.342503  \n",
+       "                  MAE                                    2.529447  \n",
+       "                  sMAPE                                 99.820589  \n",
+       "                  MASE                                   4.438016  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1769,7 +1643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "622c8342",
    "metadata": {},
    "outputs": [],
@@ -1779,7 +1653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "6c87c037",
    "metadata": {},
    "outputs": [],
@@ -1793,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "9e46a81e",
    "metadata": {},
    "outputs": [],
@@ -1805,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "da7935d5",
    "metadata": {},
    "outputs": [
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        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-b4d06d14868747c5b08ad3c32b78b991\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-b4d06d14868747c5b08ad3c32b78b991\");\n",
+       "    if (outputDiv.id !== \"altair-viz-5331a3ae8ad64826890d9a649a84307a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-5331a3ae8ad64826890d9a649a84307a\");\n",
        "    }\n",
        "\n",
        "    const paths = {\n",
@@ -1878,14 +1752,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"1990-07-29T00:00:00.000\", \"variable\": \"input\", \"value\": 7.9}, {\"time\": \"1990-07-30T00:00:00.000\", \"variable\": \"input\", \"value\": 6.0}, {\"time\": \"1990-07-31T00:00:00.000\", \"variable\": \"input\", \"value\": 5.0}, {\"time\": \"1990-08-01T00:00:00.000\", \"variable\": \"input\", \"value\": 6.8}, {\"time\": \"1990-08-02T00:00:00.000\", \"variable\": \"input\", \"value\": 9.8}, {\"time\": \"1990-08-03T00:00:00.000\", \"variable\": \"input\", \"value\": 5.7}, {\"time\": \"1990-08-04T00:00:00.000\", \"variable\": \"input\", \"value\": 8.6}, {\"time\": \"1990-08-05T00:00:00.000\", \"variable\": \"input\", \"value\": 10.6}, {\"time\": \"1990-08-06T00:00:00.000\", \"variable\": \"input\", \"value\": 7.8}, {\"time\": \"1990-08-07T00:00:00.000\", \"variable\": \"input\", \"value\": 7.7}, {\"time\": \"1990-08-08T00:00:00.000\", \"variable\": 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        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1902,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "id": "ffecb8fb",
    "metadata": {},
    "outputs": [],
@@ -1912,7 +1786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "e6c1b476",
    "metadata": {},
    "outputs": [
@@ -1921,23 +1795,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-1ac6c5e056544a5b921f3add37eb22dd.vega-embed {\n",
+       "  #altair-viz-7489b4e622854906adc6ec9d98b01590.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-1ac6c5e056544a5b921f3add37eb22dd.vega-embed details,\n",
-       "  #altair-viz-1ac6c5e056544a5b921f3add37eb22dd.vega-embed details summary {\n",
+       "  #altair-viz-7489b4e622854906adc6ec9d98b01590.vega-embed details,\n",
+       "  #altair-viz-7489b4e622854906adc6ec9d98b01590.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-1ac6c5e056544a5b921f3add37eb22dd\"></div>\n",
+       "<div id=\"altair-viz-7489b4e622854906adc6ec9d98b01590\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-1ac6c5e056544a5b921f3add37eb22dd\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-1ac6c5e056544a5b921f3add37eb22dd\");\n",
+       "    if (outputDiv.id !== \"altair-viz-7489b4e622854906adc6ec9d98b01590\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7489b4e622854906adc6ec9d98b01590\");\n",
        "    }\n",
        "\n",
        "    const paths = {\n",
@@ -1985,14 +1859,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
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        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
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@@ -2009,7 +1883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "id": "4bc66495",
    "metadata": {},
    "outputs": [],
@@ -2021,7 +1895,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": 18,
    "id": "6312ee42",
    "metadata": {},
    "outputs": [
@@ -2030,23 +1904,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-793086297e9e42c6817bb186899bc700.vega-embed {\n",
+       "  #altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-793086297e9e42c6817bb186899bc700.vega-embed details,\n",
-       "  #altair-viz-793086297e9e42c6817bb186899bc700.vega-embed details summary {\n",
+       "  #altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4.vega-embed details,\n",
+       "  #altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-793086297e9e42c6817bb186899bc700\"></div>\n",
+       "<div id=\"altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-793086297e9e42c6817bb186899bc700\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-793086297e9e42c6817bb186899bc700\");\n",
+       "    if (outputDiv.id !== \"altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-b3bb945e9f75429bbc66eac52cf0f0e4\");\n",
        "    }\n",
        "\n",
        "    const paths = {\n",
@@ -2094,14 +1968,14 @@
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        "        .then(() => displayChart(vegaEmbed));\n",
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\"category\"}], \"axes\": [{\"scale\": \"x\", \"grid\": true, \"maxExtent\": 0, \"minExtent\": 0, \"zindex\": 0, \"labels\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"ticks\": false, \"domain\": false, \"gridScale\": \"y\", \"aria\": false, \"orient\": \"bottom\"}, {\"scale\": \"y\", \"domain\": false, \"aria\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"orient\": \"left\", \"grid\": true, \"labels\": false, \"maxExtent\": 0, \"zindex\": 0, \"ticks\": false, \"gridScale\": \"x\", \"minExtent\": 0}, {\"scale\": \"x\", \"labelFlush\": true, \"grid\": false, \"zindex\": 0, \"orient\": \"bottom\", \"labelOverlap\": true, \"title\": \"time\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}}, {\"scale\": \"y\", \"title\": \"value\", \"grid\": false, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"orient\": \"left\", \"labelOverlap\": true}], \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}], \"width\": 600, \"style\": \"cell\", \"padding\": 5, \"background\": \"white\", \"height\": 200}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/user_guide/2_context/2.0-context-common.ipynb b/docs/user_guide/2_context/2.0-context-common.ipynb
index dad2d67..f4e6ea6 100644
--- a/docs/user_guide/2_context/2.0-context-common.ipynb
+++ b/docs/user_guide/2_context/2.0-context-common.ipynb
@@ -72,16 +72,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n",
-      "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import ontime as on"
    ]
@@ -335,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "9e34e371-9d34-41e8-8145-116c6bc463d3",
    "metadata": {
     "papermill": {
@@ -349,39 +340,104 @@
    },
    "outputs": [],
    "source": [
-    "day_mean = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEAN)\n",
-    "day_median = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEDIAN)"
+    "day_mean = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEAN).rename({\"value\": \"day_mean\"})\n",
+    "day_median = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEDIAN).rename({\"value\": \"day_median\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "fc72abf9-f1d9-4757-80c5-ecffa37d756f",
-   "metadata": {
-    "papermill": {
-     "duration": 0.214484,
-     "end_time": "2024-01-31T17:49:33.896842",
-     "exception": false,
-     "start_time": "2024-01-31T17:49:33.682358",
-     "status": "completed"
-    },
-    "tags": []
-   },
+   "execution_count": 17,
+   "id": "78d1b019",
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-ffdec8a7977c4850ab755760e11aa683.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-ffdec8a7977c4850ab755760e11aa683.vega-embed details,\n",
+       "  #altair-viz-ffdec8a7977c4850ab755760e11aa683.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-ffdec8a7977c4850ab755760e11aa683\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-ffdec8a7977c4850ab755760e11aa683\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-ffdec8a7977c4850ab755760e11aa683\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-01-01T00:00:00.000\", \"variable\": \"day_mean\", \"value\": 246.25409836065575}, {\"time\": \"2015-01-01T01:00:00.000\", \"variable\": \"day_mean\", \"value\": 221.65479452054794}, {\"time\": \"2015-01-01T02:00:00.000\", \"variable\": \"day_mean\", \"value\": 198.44383561643835}, {\"time\": \"2015-01-01T03:00:00.000\", \"variable\": \"day_mean\", \"value\": 174.75616438356164}, {\"time\": \"2015-01-01T04:00:00.000\", \"variable\": \"day_mean\", \"value\": 136.73698630136985}, {\"time\": \"2015-01-01T05:00:00.000\", \"variable\": \"day_mean\", \"value\": 137.14246575342466}, {\"time\": \"2015-01-01T06:00:00.000\", \"variable\": \"day_mean\", \"value\": 360.6054794520548}, {\"time\": \"2015-01-01T07:00:00.000\", \"variable\": \"day_mean\", \"value\": 1099.3287671232877}, {\"time\": \"2015-01-01T08:00:00.000\", \"variable\": \"day_mean\", \"value\": 2137.8698630136987}, {\"time\": \"2015-01-01T09:00:00.000\", \"variable\": \"day_mean\", \"value\": 2998.104109589041}, {\"time\": \"2015-01-01T10:00:00.000\", \"variable\": \"day_mean\", \"value\": 3526.0109589041094}, {\"time\": \"2015-01-01T11:00:00.000\", \"variable\": \"day_mean\", \"value\": 3759.6536203522505}, {\"time\": \"2015-01-01T12:00:00.000\", \"variable\": \"day_mean\", \"value\": 3790.418199608611}, {\"time\": \"2015-01-01T13:00:00.000\", \"variable\": \"day_mean\", \"value\": 3684.062230919765}, {\"time\": \"2015-01-01T14:00:00.000\", \"variable\": \"day_mean\", \"value\": 3409.8172211350293}, {\"time\": \"2015-01-01T15:00:00.000\", \"variable\": \"day_mean\", \"value\": 2902.5201565557727}, {\"time\": \"2015-01-01T16:00:00.000\", \"variable\": \"day_mean\", \"value\": 2188.2861056751467}, {\"time\": \"2015-01-01T17:00:00.000\", \"variable\": \"day_mean\", \"value\": 1459.8794520547945}, {\"time\": \"2015-01-01T18:00:00.000\", \"variable\": \"day_mean\", \"value\": 851.7776255707762}, {\"time\": \"2015-01-01T19:00:00.000\", \"variable\": \"day_mean\", \"value\": 478.41552511415523}, {\"time\": \"2015-01-01T20:00:00.000\", \"variable\": \"day_mean\", \"value\": 352.93698630136987}, {\"time\": \"2015-01-01T21:00:00.000\", \"variable\": \"day_mean\", \"value\": 317.63287671232877}, {\"time\": \"2015-01-01T22:00:00.000\", \"variable\": \"day_mean\", \"value\": 283.613698630137}, {\"time\": \"2015-01-01T23:00:00.000\", \"variable\": \"day_mean\", \"value\": 269.372602739726}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_1\", \"values\": [{\"time\": \"2015-01-01T00:00:00.000\", \"variable\": \"day_median\", \"value\": 91.0}, {\"time\": \"2015-01-01T01:00:00.000\", \"variable\": \"day_median\", \"value\": 74.0}, {\"time\": \"2015-01-01T02:00:00.000\", \"variable\": \"day_median\", \"value\": 68.0}, {\"time\": \"2015-01-01T03:00:00.000\", \"variable\": \"day_median\", \"value\": 62.0}, {\"time\": \"2015-01-01T04:00:00.000\", \"variable\": \"day_median\", \"value\": 60.0}, {\"time\": \"2015-01-01T05:00:00.000\", \"variable\": \"day_median\", \"value\": 64.0}, {\"time\": \"2015-01-01T06:00:00.000\", \"variable\": \"day_median\", \"value\": 171.0}, {\"time\": \"2015-01-01T07:00:00.000\", \"variable\": \"day_median\", \"value\": 849.0}, {\"time\": \"2015-01-01T08:00:00.000\", \"variable\": \"day_median\", \"value\": 1833.0}, {\"time\": \"2015-01-01T09:00:00.000\", \"variable\": \"day_median\", \"value\": 2918.0}, {\"time\": \"2015-01-01T10:00:00.000\", \"variable\": \"day_median\", \"value\": 3644.0}, {\"time\": \"2015-01-01T11:00:00.000\", \"variable\": \"day_median\", \"value\": 3934.0}, {\"time\": \"2015-01-01T12:00:00.000\", \"variable\": \"day_median\", \"value\": 3973.0}, {\"time\": \"2015-01-01T13:00:00.000\", \"variable\": \"day_median\", \"value\": 3883.0}, {\"time\": \"2015-01-01T14:00:00.000\", \"variable\": \"day_median\", \"value\": 3619.0}, {\"time\": \"2015-01-01T15:00:00.000\", \"variable\": \"day_median\", \"value\": 3051.0}, {\"time\": \"2015-01-01T16:00:00.000\", \"variable\": \"day_median\", \"value\": 2131.0}, {\"time\": \"2015-01-01T17:00:00.000\", \"variable\": \"day_median\", \"value\": 1060.0}, {\"time\": \"2015-01-01T18:00:00.000\", \"variable\": \"day_median\", \"value\": 515.0}, {\"time\": \"2015-01-01T19:00:00.000\", \"variable\": \"day_median\", \"value\": 350.0}, {\"time\": \"2015-01-01T20:00:00.000\", \"variable\": \"day_median\", \"value\": 252.0}, {\"time\": \"2015-01-01T21:00:00.000\", \"variable\": \"day_median\", \"value\": 189.0}, {\"time\": \"2015-01-01T22:00:00.000\", \"variable\": \"day_median\", \"value\": 131.0}, {\"time\": \"2015-01-01T23:00:00.000\", \"variable\": \"day_median\", \"value\": 107.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"day_mean\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"day_median\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_1_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_1\", \"name\": \"faceted_path_layer_1_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"faceted_path_layer_1_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"maxExtent\": 0, \"domain\": false, \"orient\": \"bottom\", \"aria\": false, \"minExtent\": 0, \"gridScale\": \"y\", \"grid\": true, \"labels\": false, \"ticks\": false}, {\"scale\": \"y\", \"maxExtent\": 0, \"grid\": true, \"ticks\": false, \"zindex\": 0, \"orient\": \"left\", \"gridScale\": \"x\", \"minExtent\": 0, \"aria\": false, \"domain\": false, \"labels\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}}, {\"scale\": \"x\", \"grid\": false, \"orient\": \"bottom\", \"labelFlush\": true, \"labelOverlap\": true, \"title\": \"time\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0}, {\"scale\": \"y\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"grid\": false, \"orient\": \"left\", \"title\": \"value\", \"labelOverlap\": true}], \"height\": 300, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"width\": 300, \"background\": \"white\", \"padding\": 5, \"style\": \"cell\"}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "day_mean.plot();\n",
-    "day_median.plot();"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, day_mean)\n",
+    "    .add(on.marks.line, day_median)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -403,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "e26c11bc-e46d-45ab-aab6-4e80c4288ca0",
    "metadata": {
     "papermill": {
@@ -417,13 +473,13 @@
    },
    "outputs": [],
    "source": [
-    "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n",
-    "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)"
+    "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN).rename({\"value\": \"week_mean\"})\n",
+    "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN).rename({\"value\": \"week_median\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "id": "4409d4ee-aca9-4bae-b1d1-03283554bbbe",
    "metadata": {
     "papermill": {
@@ -438,18 +494,92 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-3521972a7b5d468ca51bae91b7813ab8.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-3521972a7b5d468ca51bae91b7813ab8.vega-embed details,\n",
+       "  #altair-viz-3521972a7b5d468ca51bae91b7813ab8.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-3521972a7b5d468ca51bae91b7813ab8\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-3521972a7b5d468ca51bae91b7813ab8\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-3521972a7b5d468ca51bae91b7813ab8\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-01-01T00:00:00.000\", \"variable\": \"week_mean\", \"value\": 231.22641509433961}, {\"time\": \"2015-01-01T01:00:00.000\", \"variable\": \"week_mean\", \"value\": 202.22641509433961}, {\"time\": \"2015-01-01T02:00:00.000\", \"variable\": \"week_mean\", \"value\": 172.88679245283018}, {\"time\": \"2015-01-01T03:00:00.000\", \"variable\": \"week_mean\", \"value\": 148.8679245283019}, {\"time\": \"2015-01-01T04:00:00.000\", \"variable\": \"week_mean\", \"value\": 117.58490566037736}, {\"time\": \"2015-01-01T05:00:00.000\", \"variable\": \"week_mean\", \"value\": 130.43396226415095}, {\"time\": \"2015-01-01T06:00:00.000\", \"variable\": \"week_mean\", \"value\": 357.1320754716981}, {\"time\": \"2015-01-01T07:00:00.000\", \"variable\": \"week_mean\", \"value\": 1111.754716981132}, {\"time\": \"2015-01-01T08:00:00.000\", \"variable\": \"week_mean\", 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+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 19,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "week_mean.plot();\n",
-    "week_median.plot();"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, week_mean)\n",
+    "    .add(on.marks.line, week_median)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -471,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "e5df7b11-4c2d-4b26-9b9c-f1bf521b6300",
    "metadata": {
     "papermill": {
@@ -490,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "id": "402c4d6e-dd2c-4920-9736-eee98fa3ab32",
    "metadata": {
     "papermill": {
@@ -506,10 +636,10 @@
     {
      "data": {
       "text/plain": [
-       "<ontime.context.common.generic_predictor.GenericPredictor at 0x2c52d7f10>"
+       "<ontime.context.common.generic_predictor.GenericPredictor at 0x7fc6c9b189d0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "id": "33c52d1f-6983-4edb-945b-9578d954f738",
    "metadata": {
     "papermill": {
@@ -556,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "id": "c10dd2d7-7a48-4343-b18a-152175dcd799",
    "metadata": {
     "papermill": {
@@ -574,28 +704,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-323d36c0b96e4e358f113869f1909ce7.vega-embed {\n",
+       "  #altair-viz-93f7eae9d64a4892b898aeaeac3676e5.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-323d36c0b96e4e358f113869f1909ce7.vega-embed details,\n",
-       "  #altair-viz-323d36c0b96e4e358f113869f1909ce7.vega-embed details summary {\n",
+       "  #altair-viz-93f7eae9d64a4892b898aeaeac3676e5.vega-embed details,\n",
+       "  #altair-viz-93f7eae9d64a4892b898aeaeac3676e5.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-323d36c0b96e4e358f113869f1909ce7\"></div>\n",
+       "<div id=\"altair-viz-93f7eae9d64a4892b898aeaeac3676e5\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-323d36c0b96e4e358f113869f1909ce7\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-323d36c0b96e4e358f113869f1909ce7\");\n",
+       "    if (outputDiv.id !== \"altair-viz-93f7eae9d64a4892b898aeaeac3676e5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-93f7eae9d64a4892b898aeaeac3676e5\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -628,28 +759,36 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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\"2015-09-02T11:00:00.000\", \"variable\": \"Truth\", \"value\": 3865.0}, {\"time\": \"2015-09-02T12:00:00.000\", \"variable\": \"Truth\", \"value\": 3737.0}, {\"time\": \"2015-09-02T13:00:00.000\", \"variable\": \"Truth\", \"value\": 3644.0}, {\"time\": \"2015-09-02T14:00:00.000\", \"variable\": \"Truth\", \"value\": 3325.0}, {\"time\": \"2015-09-02T15:00:00.000\", \"variable\": \"Truth\", \"value\": 2346.0}, {\"time\": \"2015-09-02T16:00:00.000\", \"variable\": \"Truth\", \"value\": 883.0}, {\"time\": \"2015-09-02T17:00:00.000\", \"variable\": \"Truth\", \"value\": 396.0}, {\"time\": \"2015-09-02T18:00:00.000\", \"variable\": \"Truth\", \"value\": 279.0}, {\"time\": \"2015-09-02T19:00:00.000\", \"variable\": \"Truth\", \"value\": 171.0}, {\"time\": \"2015-09-02T20:00:00.000\", \"variable\": \"Truth\", \"value\": 115.0}, {\"time\": \"2015-09-02T21:00:00.000\", \"variable\": \"Truth\", \"value\": 105.0}, {\"time\": \"2015-09-02T22:00:00.000\", \"variable\": \"Truth\", \"value\": 341.0}, {\"time\": \"2015-09-02T23:00:00.000\", \"variable\": \"Truth\", \"value\": 225.0}, {\"time\": \"2015-09-03T00:00:00.000\", \"variable\": \"Truth\", \"value\": 149.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"Training set\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"Prediction\"}]}, {\"name\": \"source_2_color_domain_variable_2\", \"values\": [{\"variable\": \"Truth\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_1_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_1\", \"name\": \"faceted_path_layer_1_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"faceted_path_layer_1_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"group\", \"name\": \"layer_2_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_2\", \"name\": \"faceted_path_layer_2_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_2_marks\", \"from\": {\"data\": \"faceted_path_layer_2_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"strokeDash\": {\"value\": [5, 2]}, \"opacity\": {\"value\": 0.7}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}, {\"data\": \"source_2\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}, {\"data\": \"source_2\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}, {\"data\": \"source_2_color_domain_variable_2\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"domain\": false, \"grid\": true, \"aria\": false, \"labels\": false, \"gridScale\": \"y\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"maxExtent\": 0, \"ticks\": false, \"minExtent\": 0, \"orient\": \"bottom\", \"zindex\": 0}, {\"scale\": \"y\", \"ticks\": false, \"orient\": \"left\", \"grid\": true, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"aria\": false, \"maxExtent\": 0, \"gridScale\": \"x\", \"domain\": false, \"labels\": false, \"minExtent\": 0}, {\"scale\": \"x\", \"zindex\": 0, \"labelFlush\": true, \"title\": \"time\", \"grid\": false, \"orient\": \"bottom\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(width/40)\"}}, {\"scale\": \"y\", \"grid\": false, \"labelOverlap\": true, \"title\": \"value\", \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0}], \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}], \"style\": \"cell\", \"width\": 600, \"padding\": 5, \"height\": 300, \"background\": \"white\"}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
-       "alt.Chart(...)"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.prediction(train[-96:], pred, test[:48])"
+    "(\n",
+    "    on.Plot()\n",
+    "    .add(on.marks.line, train[-96:].rename({\"generation solar\": \"Training set\"}))\n",
+    "    .add(on.marks.line, pred.rename({\"generation solar\": \"Prediction\"}))\n",
+    "    .add(on.marks.line, test[:48].rename({\"generation solar\": \"Truth\"}), type=\"dashed\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -671,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "id": "9715fd4d-24d8-4d95-a77a-3c347916f2aa",
    "metadata": {
     "papermill": {
@@ -690,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "id": "56eae67d-ae1a-438c-bbd4-e1b52c93c2c2",
    "metadata": {
     "papermill": {
@@ -706,10 +845,10 @@
     {
      "data": {
       "text/plain": [
-       "<ontime.context.common.generic_detector.GenericDetector at 0x2c53e4b50>"
+       "<ontime.context.common.generic_detector.GenericDetector at 0x7fc6c9a32ef0>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -737,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "id": "3fcf48b4-6d60-411d-88e5-191d575967a7",
    "metadata": {
     "papermill": {
@@ -756,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "id": "317ef652-cfa1-4d85-b873-a1944c13fefc",
    "metadata": {
     "papermill": {
@@ -774,28 +913,29 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-d589638a81314835972557fc166ac515.vega-embed {\n",
+       "  #altair-viz-49d70762ee724c358b2742671bab69d5.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-d589638a81314835972557fc166ac515.vega-embed details,\n",
-       "  #altair-viz-d589638a81314835972557fc166ac515.vega-embed details summary {\n",
+       "  #altair-viz-49d70762ee724c358b2742671bab69d5.vega-embed details,\n",
+       "  #altair-viz-49d70762ee724c358b2742671bab69d5.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-d589638a81314835972557fc166ac515\"></div>\n",
+       "<div id=\"altair-viz-49d70762ee724c358b2742671bab69d5\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-d589638a81314835972557fc166ac515\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-d589638a81314835972557fc166ac515\");\n",
+       "    if (outputDiv.id !== \"altair-viz-49d70762ee724c358b2742671bab69d5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-49d70762ee724c358b2742671bab69d5\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
        "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
        "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
@@ -828,28 +968,35 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"anomaly\": 1.0, \"signal\": 53.0, \"time\": \"2015-09-01T01:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 42.0, \"time\": \"2015-09-01T02:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 36.0, \"time\": \"2015-09-01T03:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 35.0, \"time\": \"2015-09-01T04:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 35.0, \"time\": \"2015-09-01T05:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 55.0, \"time\": \"2015-09-01T06:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 704.0, \"time\": \"2015-09-01T07:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 1889.0, \"time\": \"2015-09-01T08:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 3095.0, \"time\": \"2015-09-01T09:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3712.0, \"time\": \"2015-09-01T10:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3901.0, \"time\": \"2015-09-01T11:00:00.000\"}, {\"anomaly\": 1.0, 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{\"anomaly\": 1.0, \"signal\": 73.0, \"time\": \"2015-09-02T01:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 72.0, \"time\": \"2015-09-02T02:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 73.0, \"time\": \"2015-09-02T03:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 73.0, \"time\": \"2015-09-02T04:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 80.0, \"time\": \"2015-09-02T05:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 218.0, \"time\": \"2015-09-02T06:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 1296.0, \"time\": \"2015-09-02T07:00:00.000\"}, {\"anomaly\": 0.0, \"signal\": 2524.0, \"time\": \"2015-09-02T08:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3665.0, \"time\": \"2015-09-02T09:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3939.0, \"time\": \"2015-09-02T10:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3865.0, \"time\": \"2015-09-02T11:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3737.0, \"time\": \"2015-09-02T12:00:00.000\"}, {\"anomaly\": 1.0, \"signal\": 3644.0, \"time\": 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{\"mode\": \"vega\"});\n",
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\"cell\", \"padding\": 5, \"width\": 600, \"height\": 300}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.anomalies(test[:72], detected_test[:72])"
+    "(\n",
+    "    on.Plot(test[:72])\n",
+    "    .add(on.marks.line)\n",
+    "    .add(on.marks.mark, data=detected_test[:72].rename({\"generation solar\": \"Anomalies\"}), type=\"dot\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -871,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
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@@ -890,7 +1037,7 @@
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+   "execution_count": 32,
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-       "  #altair-viz-ebd839e7f3074e1288418c8c8e577892.vega-embed details summary {\n",
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\"generation solar\", \"value\": 17.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_1\", \"values\": [{\"time\": \"2015-09-01T01:00:00.000\", \"value\": 53.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T02:00:00.000\", \"value\": 42.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T03:00:00.000\", \"value\": 36.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T04:00:00.000\", \"value\": 35.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T05:00:00.000\", \"value\": 35.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T06:00:00.000\", \"value\": 55.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T07:00:00.000\", \"value\": 704.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T21:00:00.000\", \"value\": 56.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T22:00:00.000\", \"value\": 84.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-01T23:00:00.000\", \"value\": 73.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T00:00:00.000\", \"value\": 73.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T01:00:00.000\", \"value\": 73.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T02:00:00.000\", \"value\": 72.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T03:00:00.000\", \"value\": 73.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T04:00:00.000\", \"value\": 73.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T18:00:00.000\", \"value\": 279.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T19:00:00.000\", \"value\": 171.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T20:00:00.000\", \"value\": 115.0, \"variable\": \"Anomalies\"}, {\"time\": \"2015-09-02T21:00:00.000\", \"value\": 105.0, \"variable\": \"Anomalies\"}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_color_domain_variable_0\", \"values\": [{\"variable\": \"generation solar\"}]}, {\"name\": \"source_1_color_domain_variable_1\", \"values\": [{\"variable\": \"Anomalies\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}, {\"type\": \"symbol\", \"name\": \"layer_1_marks\", \"from\": {\"data\": \"source_1\"}, \"encode\": {\"update\": {\"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"shape\": {\"value\": \"circle\"}, \"opacity\": {\"value\": 0.7}, \"fill\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"circle\"]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"time\"}, {\"data\": \"source_1\", \"field\": \"time\"}]}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"fields\": [{\"data\": \"source_0\", \"field\": \"value\"}, {\"data\": \"source_1\", \"field\": \"value\"}]}, \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"fields\": [{\"data\": \"source_0_color_domain_variable_0\", \"field\": \"variable\"}, {\"data\": \"source_1_color_domain_variable_1\", \"field\": \"variable\"}], \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"maxExtent\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"aria\": false, \"labels\": false, \"ticks\": false, \"domain\": false, \"minExtent\": 0, \"orient\": \"bottom\", \"gridScale\": \"y\", \"grid\": true}, {\"scale\": \"y\", \"minExtent\": 0, \"gridScale\": \"x\", \"ticks\": false, \"labels\": false, \"maxExtent\": 0, \"aria\": false, \"orient\": \"left\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"grid\": true, \"domain\": false}, {\"scale\": \"x\", \"labelFlush\": true, \"labelOverlap\": true, \"grid\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"orient\": \"bottom\", \"title\": \"time\"}, {\"scale\": \"y\", \"orient\": \"left\", \"labelOverlap\": true, \"grid\": false, \"title\": \"value\", \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}}], \"background\": \"white\", \"padding\": 5, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"circle\", \"title\": \"variable\", \"fill\": \"color\", \"encode\": {\"symbols\": {\"update\": {\"opacity\": {\"value\": 0.7}}}}}], \"width\": 600, \"height\": 300, \"style\": \"cell\"}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "on.plots.anomalies(test[:72], predetected[:72])"
+    "(\n",
+    "    on.Plot(test[:72])\n",
+    "    .add(on.marks.line)\n",
+    "    .add(on.marks.mark, data=predetected[:72].rename({\"generation solar\": \"Anomalies\"}), type=\"dot\")\n",
+    "    .properties(width=600, height=300)\n",
+    "    .show()\n",
+    ")"
    ]
   },
   {
@@ -1013,19 +1168,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "id": "9ece9880-4d13-4489-8e67-a755d10caab5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-2a1bdfd1ded44295a734de86a5f6f713.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-2a1bdfd1ded44295a734de86a5f6f713.vega-embed details,\n",
+       "  #altair-viz-2a1bdfd1ded44295a734de86a5f6f713.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-2a1bdfd1ded44295a734de86a5f6f713\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-2a1bdfd1ded44295a734de86a5f6f713\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-2a1bdfd1ded44295a734de86a5f6f713\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-09-01T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-04T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": 0.0, \"max\": 1.0}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"generation solar\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"aria\": false, \"ticks\": false, \"minExtent\": 0, \"domain\": false, \"orient\": \"bottom\", \"zindex\": 0, \"labels\": false, \"gridScale\": \"y\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": true, \"maxExtent\": 0}, {\"scale\": \"y\", \"domain\": false, \"ticks\": false, \"grid\": true, \"labels\": false, \"maxExtent\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"gridScale\": \"x\", \"aria\": false, \"minExtent\": 0, \"zindex\": 0, \"orient\": \"left\"}, {\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"orient\": \"bottom\", \"zindex\": 0, \"labelOverlap\": true, \"labelFlush\": true, \"title\": \"time\"}, {\"scale\": \"y\", \"title\": \"value\", \"labelOverlap\": true, \"orient\": \"left\", \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0}], \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}], \"height\": 200, \"width\": 400, \"background\": \"white\", \"style\": \"cell\", \"padding\": 5}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 33,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1038,7 +1263,7 @@
     "detector.fit(test)\n",
     "\n",
     "# Detecting on other\n",
-    "detector.detect(test[:72]).plot();"
+    "detector.detect(test[:72]).plot()"
    ]
   },
   {
@@ -1051,19 +1276,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "id": "70588b2e-70fd-415b-b51a-8f6ba3bbb48e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-81e53ff73b5a4d9ba959478004f1ce13.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-81e53ff73b5a4d9ba959478004f1ce13.vega-embed details,\n",
+       "  #altair-viz-81e53ff73b5a4d9ba959478004f1ce13.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-81e53ff73b5a4d9ba959478004f1ce13\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-81e53ff73b5a4d9ba959478004f1ce13\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-81e53ff73b5a4d9ba959478004f1ce13\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-09-01T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-04T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": 0.0, \"max\": 1.0}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"generation solar\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"orient\": \"bottom\", \"gridScale\": \"y\", \"grid\": true, \"aria\": false, \"labels\": false, \"ticks\": false, \"domain\": false, \"minExtent\": 0, \"zindex\": 0, \"maxExtent\": 0}, {\"scale\": \"y\", \"grid\": true, \"ticks\": false, \"zindex\": 0, \"gridScale\": \"x\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"maxExtent\": 0, \"aria\": false, \"orient\": \"left\", \"domain\": false, \"labels\": false, \"minExtent\": 0}, {\"scale\": \"x\", \"grid\": false, \"labelOverlap\": true, \"labelFlush\": true, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"title\": \"time\", \"orient\": \"bottom\"}, {\"scale\": \"y\", \"orient\": \"left\", \"title\": \"value\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"grid\": false, \"zindex\": 0, \"labelOverlap\": true}], \"width\": 400, \"padding\": 5, \"style\": \"cell\", \"height\": 200, \"background\": \"white\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}]}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 34,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1077,7 +1372,7 @@
     "detector.fit(test)\n",
     "\n",
     "# Detecting on other\n",
-    "detector.detect(test[:72]).plot();"
+    "detector.detect(test[:72]).plot()"
    ]
   },
   {
@@ -1090,19 +1385,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "id": "690c0441-c305-491a-91ac-83bd0908e38c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-f06773f54bc24d62acb7c4b386d3bcae.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-f06773f54bc24d62acb7c4b386d3bcae.vega-embed details,\n",
+       "  #altair-viz-f06773f54bc24d62acb7c4b386d3bcae.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-f06773f54bc24d62acb7c4b386d3bcae\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-f06773f54bc24d62acb7c4b386d3bcae\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f06773f54bc24d62acb7c4b386d3bcae\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-09-01T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-02T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-02T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-04T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": 0.0, \"max\": 1.0}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"generation solar\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}, \"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"zero\": true, \"nice\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"minExtent\": 0, \"gridScale\": \"y\", \"maxExtent\": 0, \"grid\": true, \"ticks\": false, \"labels\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"domain\": false, \"orient\": \"bottom\", \"zindex\": 0, \"aria\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"labels\": false, \"gridScale\": \"x\", \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"aria\": false, \"maxExtent\": 0, \"minExtent\": 0, \"zindex\": 0, \"ticks\": false, \"domain\": false, \"grid\": true}, {\"scale\": \"x\", \"orient\": \"bottom\", \"labelOverlap\": true, \"labelFlush\": true, \"title\": \"time\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"zindex\": 0}, {\"scale\": \"y\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"orient\": \"left\", \"zindex\": 0, \"grid\": false, \"title\": \"value\"}], \"style\": \"cell\", \"padding\": 5, \"height\": 200, \"background\": \"white\", \"width\": 400, \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}]}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 35,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1115,7 +1480,7 @@
     "detector.fit(test)\n",
     "\n",
     "# Detecting on other\n",
-    "detector.detect(test[:72]).plot();"
+    "detector.detect(test[:72]).plot()"
    ]
   },
   {
@@ -1136,7 +1501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 36,
    "id": "f1861ec2-8b00-4197-b9c6-9efe7851b6ff",
    "metadata": {},
    "outputs": [],
@@ -1162,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 37,
    "id": "5117df37-64c4-470b-b102-343a6be554db",
    "metadata": {},
    "outputs": [],
@@ -1180,39 +1545,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 38,
    "id": "bac3e163-2661-4997-835e-0bd815b5be03",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-17102feb414440c7a8faef3867746b91.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-17102feb414440c7a8faef3867746b91.vega-embed details,\n",
+       "  #altair-viz-17102feb414440c7a8faef3867746b91.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-17102feb414440c7a8faef3867746b91\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-17102feb414440c7a8faef3867746b91\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-17102feb414440c7a8faef3867746b91\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"time\": \"2015-09-01T01:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T02:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T03:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T04:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-01T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-01T20:00:00.000\", \"variable\": \"generation solar\", 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\"value\": 0.0}, {\"time\": \"2015-09-03T05:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T06:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T07:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T08:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T09:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T10:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T11:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T12:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T13:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T14:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T15:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T16:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T17:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T18:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T19:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T20:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T21:00:00.000\", \"variable\": \"generation solar\", \"value\": 1.0}, {\"time\": \"2015-09-03T22:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-03T23:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}, {\"time\": \"2015-09-04T00:00:00.000\", \"variable\": \"generation solar\", \"value\": 0.0}], \"transform\": [{\"type\": \"formula\", \"expr\": \"toDate(datum['time'])\", \"as\": \"time\"}]}, {\"name\": \"source_0_y_domain_value\", \"values\": [{\"min\": 0.0, \"max\": 1.0}]}, {\"name\": \"source_0_color_domain_variable\", \"values\": [{\"variable\": \"generation solar\"}]}], \"marks\": [{\"type\": \"group\", \"name\": \"layer_0_pathgroup\", \"from\": {\"facet\": {\"data\": \"source_0\", \"name\": \"faceted_path_layer_0_main\", \"groupby\": [\"variable\"]}}, \"encode\": {\"update\": {\"width\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"width\", \"parent\": null}}, \"height\": {\"field\": {\"signal\": null, \"datum\": null, \"group\": \"height\", \"parent\": null}}}}, \"marks\": [{\"type\": \"line\", \"name\": \"layer_0_marks\", \"from\": {\"data\": \"faceted_path_layer_0_main\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"field\": \"variable\", \"scale\": \"color\"}, \"x\": {\"field\": \"time\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"time\\\"]) && isFinite(+datum[\\\"time\\\"]) && isValid(datum[\\\"value\\\"]) && isFinite(+datum[\\\"value\\\"])\"}, \"y\": {\"field\": \"value\", \"scale\": \"y\"}}}, \"style\": [\"line\"]}]}], \"scales\": [{\"name\": \"x\", \"type\": \"time\", \"domain\": {\"data\": \"source_0\", \"field\": \"time\"}, \"range\": [0, {\"signal\": \"width\"}]}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_value\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}, {\"name\": \"color\", \"type\": \"ordinal\", \"domain\": {\"data\": \"source_0_color_domain_variable\", \"field\": \"variable\", \"sort\": true}, \"range\": \"category\"}], \"axes\": [{\"scale\": \"x\", \"ticks\": false, \"domain\": false, \"maxExtent\": 0, \"gridScale\": \"y\", \"zindex\": 0, \"labels\": false, \"aria\": false, \"minExtent\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"orient\": \"bottom\", \"grid\": true}, {\"scale\": \"y\", \"orient\": \"left\", \"grid\": true, \"ticks\": false, \"aria\": false, \"minExtent\": 0, \"maxExtent\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"domain\": false, \"labels\": false, \"gridScale\": \"x\"}, {\"scale\": \"x\", \"labelFlush\": true, \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"title\": \"time\", \"labelOverlap\": true, \"zindex\": 0}, {\"scale\": \"y\", \"grid\": false, \"labelOverlap\": true, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"orient\": \"left\", \"title\": \"value\"}], \"padding\": 5, \"width\": 400, \"background\": \"white\", \"height\": 200, \"style\": \"cell\", \"legends\": [{\"stroke\": \"color\", \"symbolType\": \"stroke\", \"title\": \"variable\"}]}, {\"mode\": \"vega\"});\n",
+       "</script>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "alt.LayerChart(...)"
       ]
      },
+     "execution_count": 38,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "detector = common.MissingDataDetector()\n",
     "\n",
-    "detector.detect(ts_w_nans[:72]).plot();"
+    "detector.detect(ts_w_nans[:72]).plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6b18712-527c-4255-b287-cd67b38f25c9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ontime-2OQVvbNf-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -1226,7 +1653,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/poetry.lock b/poetry.lock
index d73b206..6ba6c02 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
 [[package]]
 name = "about-time"
@@ -153,7 +153,6 @@ files = [
 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
@@ -452,17 +451,6 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
-[[package]]
-name = "async-timeout"
-version = "5.0.1"
-description = "Timeout context manager for asyncio programs"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
-    {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
-]
-
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -1235,6 +1223,24 @@ files = [
     {file = "docutils-0.21.2.tar.gz", hash = "sha256:3a6b18732edf182daa3cd12775bbb338cf5691468f91eeeb109deff6ebfa986f"},
 ]
 
+[[package]]
+name = "easydev"
+version = "0.13.2"
+description = "Commn utilities to ease development of Python packages"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "easydev-0.13.2-py3-none-any.whl", hash = "sha256:905ce60d85cdd0236d13eea42b517f757fcf79491fbc793b20d8d4d39e7f81f0"},
+    {file = "easydev-0.13.2.tar.gz", hash = "sha256:a5c054dbf14e923927874fec304de9d988e19c4174cd10bd671ab7a21004098c"},
+]
+
+[package.dependencies]
+colorama = ">=0.4.6,<0.5.0"
+colorlog = ">=6.8.2,<7.0.0"
+line-profiler = ">=4.1.2,<5.0.0"
+pexpect = ">=4.9.0,<5.0.0"
+platformdirs = ">=4.2.0,<5.0.0"
+
 [[package]]
 name = "entrypoints"
 version = "0.4"
@@ -2724,6 +2730,73 @@ cli = ["fire"]
 docs = ["requests (>=2.0.0)"]
 typing = ["mypy (>=1.0.0)", "types-setuptools"]
 
+[[package]]
+name = "line-profiler"
+version = "4.2.0"
+description = "Line-by-line profiler"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "line_profiler-4.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b6047c8748d7a2453522eaea3edc8d9febc658b57f2ea189c03fe3d5e34595b5"},
+    {file = "line_profiler-4.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0048360a2afbd92c0b423f8207af1f6581d85c064c0340b0d02c63c8e0c8292c"},
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+    {file = "line_profiler-4.2.0.tar.gz", hash = "sha256:09e10f25f876514380b3faee6de93fb0c228abba85820ba1a591ddb3eb451a96"},
+]
+
+[package.extras]
+all = ["Cython (>=3.0.3)", "IPython (>=7.14.0)", "IPython (>=7.18.0)", "IPython (>=8.12.2)", "IPython (>=8.14.0)", "cibuildwheel (>=2.11.2)", "cibuildwheel (>=2.11.2)", "cibuildwheel (>=2.11.2)", "cibuildwheel (>=2.11.2)", "cibuildwheel (>=2.11.2)", "cibuildwheel (>=2.8.1)", "cmake (>=3.21.2)", "coverage[toml] (>=6.1.1)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=7.3.0)", "ninja (>=1.10.2)", "pytest (>=6.2.5)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest-cov (>=3.0.0)", "rich (>=12.3.0)", "scikit-build (>=0.11.1)", "setuptools (>=41.0.1)", "setuptools (>=68.2.2)", "ubelt (>=1.3.4)", "xdoctest (>=1.1.3)"]
+all-strict = ["Cython (==3.0.3)", "IPython (==7.14.0)", "IPython (==7.18.0)", "IPython (==8.12.2)", "IPython (==8.14.0)", "cibuildwheel (==2.11.2)", "cibuildwheel (==2.11.2)", "cibuildwheel (==2.11.2)", "cibuildwheel (==2.11.2)", "cibuildwheel (==2.11.2)", "cibuildwheel (==2.8.1)", "cmake (==3.21.2)", "coverage[toml] (==6.1.1)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==7.3.0)", "ninja (==1.10.2)", "pytest (==6.2.5)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest-cov (==3.0.0)", "rich (==12.3.0)", "scikit-build (==0.11.1)", "setuptools (==41.0.1)", "setuptools (==68.2.2)", "ubelt (==1.3.4)", "xdoctest (==1.1.3)"]
+ipython = ["IPython (>=7.14.0)", "IPython (>=7.18.0)", "IPython (>=8.12.2)", "IPython (>=8.14.0)"]
+ipython-strict = ["IPython (==7.14.0)", "IPython (==7.18.0)", "IPython (==8.12.2)", "IPython (==8.14.0)"]
+optional = ["IPython (>=7.14.0)", "IPython (>=7.18.0)", "IPython (>=8.12.2)", "IPython (>=8.14.0)", "rich (>=12.3.0)"]
+optional-strict = ["IPython (==7.14.0)", "IPython (==7.18.0)", "IPython (==8.12.2)", "IPython (==8.14.0)", "rich (==12.3.0)"]
+tests = ["coverage[toml] (>=6.1.1)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=6.5.0)", "coverage[toml] (>=7.3.0)", "pytest (>=6.2.5)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest (>=7.4.4)", "pytest-cov (>=3.0.0)", "ubelt (>=1.3.4)", "xdoctest (>=1.1.3)"]
+tests-strict = ["coverage[toml] (==6.1.1)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==6.5.0)", "coverage[toml] (==7.3.0)", "pytest (==6.2.5)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest (==7.4.4)", "pytest-cov (==3.0.0)", "ubelt (==1.3.4)", "xdoctest (==1.1.3)"]
+
 [[package]]
 name = "livereload"
 version = "2.7.0"
@@ -3335,9 +3408,6 @@ files = [
     {file = "multidict-6.1.0.tar.gz", hash = "sha256:22ae2ebf9b0c69d206c003e2f6a914ea33f0a932d4aa16f236afc049d9958f4a"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
-
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -4211,6 +4281,30 @@ numpy = ">=1.4"
 [package.extras]
 test = ["pytest", "pytest-cov", "scipy"]
 
+[[package]]
+name = "periodicity-detection"
+version = "0.1.3"
+description = "Detect the dominant period in univariate, equidistant time series data."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "periodicity_detection-0.1.3-py3-none-any.whl", hash = "sha256:ec818870ee8d3b2504ac09d77d6ffcaaa88d3c15cdf932840bc2fb9772f57a4a"},
+    {file = "periodicity_detection-0.1.3.tar.gz", hash = "sha256:42592710a75c5b6d6b69bb6d96ade269ecf3affb6a62c92de99a099b409c2cff"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.5.3"
+numpy = ">=1.21.6"
+pandas = ">=1.3.5"
+scipy = ">=1.7.3"
+spectrum = {version = ">=0.8", markers = "python_version >= \"3.8\""}
+statsmodels = ">=0.13.5"
+typing-extensions = ">=4.7.1"
+
+[package.extras]
+dev = ["black (==23.1)", "flake8 (==5.0.4)", "mypy (==0.920)", "pre-commit (==2.21)", "pytest", "pytest-cov"]
+docs = ["myst-parser (==0.18.0)", "sphinx (==5.0.2)", "sphinx-rtd-dark-mode (==1.2.4)", "sphinx-rtd-theme (==1.0.0)"]
+
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -5626,6 +5720,27 @@ files = [
     {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"},
 ]
 
+[[package]]
+name = "spectrum"
+version = "0.9.0"
+description = "Spectrum Analysis Tools"
+optional = false
+python-versions = "*"
+files = [
+    {file = "spectrum-0.9.0.tar.gz", hash = "sha256:4539347e7cdb9ea4ea63ca76033eed8bf54f283dc4a42e966464734c279809cd"},
+]
+
+[package.dependencies]
+easydev = "*"
+matplotlib = "*"
+numpy = "*"
+scipy = "*"
+
+[package.extras]
+doc = ["sphinx", "sphinx_rtd_theme"]
+plot = ["matplotlib"]
+testing = ["coveralls", "pytest", "pytest-cov", "pytest-mock", "pytest-runner", "pytest-timeout", "pytest-xdist"]
+
 [[package]]
 name = "sphinx"
 version = "7.4.7"
@@ -6951,4 +7066,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "2e8ca16da0fa7ffc9948d12d4bb1cb8a65217bfebc78445c6945560ac44d78af"
+content-hash = "216c06551c9110deba49187740be71cc669e5b4f76af42081bf279a480eca4e1"
diff --git a/pyproject.toml b/pyproject.toml
index 523eed3..b0d7dca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ lightning = "^2.2.4"
 numpy = "^1.26.4"
 calmap = "^0.0.11"
 alive-progress = "^3.2.0"
+periodicity-detection = "^0.1.3"
 
 [tool.poetry.group.dev]
 optional = true
diff --git a/src/ontime/core/__init__.py b/src/ontime/core/__init__.py
index da8badf..c49039c 100644
--- a/src/ontime/core/__init__.py
+++ b/src/ontime/core/__init__.py
@@ -1,6 +1,6 @@
 from .detection import detectors, abstract_detector
 from .generation import generators, abstract_generator
-from .modelling import Model, model_interface, models
+from .modelling import Model, abstract_model, models
 from .plotting import Plot, marks
 from .processing import processors, abstract_processor
 from .time_series import TimeSeries
diff --git a/src/ontime/core/modelling/registry/autoencoder.py b/src/ontime/core/modelling/registry/autoencoder.py
index 111f694..94b9902 100644
--- a/src/ontime/core/modelling/registry/autoencoder.py
+++ b/src/ontime/core/modelling/registry/autoencoder.py
@@ -6,13 +6,9 @@
 import torch.nn.functional as F
 import pandas as pd
 from torch import Tensor
-import periodicity_detection as pyd
-from torch.utils.data import DataLoader
 
 from ontime.core.time_series import TimeSeries
 from ontime.module.processing.pytorch.sliced_dataset import SlicedDataset
-from ontime.module.benchmarking.benchmark import Benchmark
-
 
 device = "cpu"