diff --git a/poetry.lock b/poetry.lock
index 7f52fd3..6667ae4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -31,112 +31,140 @@ tests = ["hypothesis", "pytest"]
 
 [[package]]
 name = "adagio"
-version = "0.2.4"
+version = "0.2.6"
 description = "The Dag IO Framework for Fugue projects"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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-    {file = "adagio-0.2.4.tar.gz", hash = "sha256:e58abc4539184a65faf9956957d3787616bedeb1303ac5c9b1a201d8af6b87d7"},
+    {file = "adagio-0.2.6-py3-none-any.whl", hash = "sha256:1bb8317d41bfff8b11373bc03c9859ff166c498214bb2b7ce1e21638c0babb2c"},
+    {file = "adagio-0.2.6.tar.gz", hash = "sha256:0c32768f3aba0e05273b36f9420a482034f2510f059171040d7e98ba34128d7a"},
 ]
 
 [package.dependencies]
 triad = ">=0.6.1"
 
 [[package]]
-name = "aiohttp"
-version = "3.9.5"
-description = "Async http client/server framework (asyncio)"
+name = "aiohappyeyeballs"
+version = "2.4.0"
+description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
-[package.dependencies]
+[[package]]
+name = "aiohttp"
+version = "3.10.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
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+]
+
+[package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -184,28 +212,26 @@ tz = ["backports.zoneinfo"]
 
 [[package]]
 name = "altair"
-version = "5.3.0"
+version = "5.4.0"
 description = "Vega-Altair: A declarative statistical visualization library for Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "altair-5.4.0.tar.gz", hash = "sha256:27c69e93d85b7bb3c98fa3626ef7e6bc6939a1466a55a8f8bf68c4bff31cf030"},
 ]
 
 [package.dependencies]
 jinja2 = "*"
 jsonschema = ">=3.0"
-numpy = "*"
+narwhals = ">=1.1.0"
 packaging = "*"
-pandas = ">=0.25"
-toolz = "*"
-typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
 
 [package.extras]
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-doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=0.25.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.6.0)"]
+dev = ["geopandas", "hatch", "ibis-framework[polars]", "ipython[kernel]", "mistune", "mypy", "pandas (>=0.25.3)", "pandas-stubs", "polars (>=0.20.3)", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.5.7)", "types-jsonschema", "types-setuptools"]
+doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx (>=8.0.0)", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
 
 [[package]]
 name = "altair-data-server"
@@ -251,24 +277,25 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "3.7.1"
+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.dependencies]
-exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
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-trio = ["trio (<0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "appdirs"
@@ -415,34 +442,45 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
+[[package]]
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+version = "4.0.3"
+description = "Timeout context manager for asyncio programs"
+optional = false
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+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
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+version = "2.16.0"
 description = "Internationalization utilities"
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 python-versions = ">=3.8"
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 [[package]]
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 [[package]]
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 [package.dependencies]
@@ -2294,7 +2341,7 @@ dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov",
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 name = "jupyterlab-server"
-version = "2.27.2"
+version = "2.27.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.27.2-py3-none-any.whl", hash = "sha256:54aa2d64fd86383b5438d9f0c032f043c4d8c0264b8af9f60bd061157466ea43"},
-    {file = "jupyterlab_server-2.27.2.tar.gz", hash = "sha256:15cbb349dc45e954e09bacf81b9f9bcb10815ff660fb2034ecd7417db3a7ea27"},
+    {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
+    {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
 ]
 
 [package.dependencies]
@@ -2345,13 +2392,13 @@ files = [
 
 [[package]]
 name = "keras"
-version = "3.4.1"
+version = "3.5.0"
 description = "Multi-backend Keras."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "keras-3.4.1-py3-none-any.whl", hash = "sha256:15599c51e2090c12f39de6db6489a0cf265ddf6653f0731b82db5af2bfa19105"},
-    {file = "keras-3.4.1.tar.gz", hash = "sha256:34cd9aeaa008914715149234c215657ca758e1b473bd2aab2e211ac967d1f8fe"},
+    {file = "keras-3.5.0-py3-none-any.whl", hash = "sha256:d37a3c623935713473ceb25241b52bce9d1e0ff5b36e5d0f6f47ed55f8500c9a"},
+    {file = "keras-3.5.0.tar.gz", hash = "sha256:53ae4f9472ec9d9c6941c82a3fda86969724ace3b7630a94ba0a1f17ba1065c3"},
 ]
 
 [package.dependencies]
@@ -2497,58 +2544,55 @@ files = [
 
 [[package]]
 name = "lightning"
-version = "2.3.3"
+version = "2.4.0"
 description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "lightning-2.3.3-py3-none-any.whl", hash = "sha256:5b4950f20b5117f30aad7820709cfa56669567cf4ded93df502b3dee443a6a5d"},
-    {file = "lightning-2.3.3.tar.gz", hash = "sha256:7f454711895c1c6e455766f01fa39522e25e5ab54c15c5e5fbad342fa92bc93c"},
+    {file = "lightning-2.4.0-py3-none-any.whl", hash = "sha256:560163af9711cf59055c448232c473150a299089efce0d2be3cc3288082d8768"},
+    {file = "lightning-2.4.0.tar.gz", hash = "sha256:9156604cc56e4b2b603f34fa7f0fe5107375c8e6d85e74544b319a15faa9ed0e"},
 ]
 
 [package.dependencies]
 fsspec = {version = ">=2022.5.0,<2026.0", extras = ["http"]}
 lightning-utilities = ">=0.10.0,<2.0"
-numpy = ">=1.17.2,<3.0"
 packaging = ">=20.0,<25.0"
 pytorch-lightning = "*"
 PyYAML = ">=5.4,<8.0"
-torch = ">=2.0.0,<4.0"
+torch = ">=2.1.0,<4.0"
 torchmetrics = ">=0.7.0,<3.0"
 tqdm = ">=4.57.0,<6.0"
 typing-extensions = ">=4.4.0,<6.0"
 
 [package.extras]
-all = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)", "hydra-core (>=1.2.0,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
-app = ["lightning-app (>=2.3.3,<3.0)"]
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 data = ["litdata (>=0.2.0rc,<1.0)"]
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-fabric-examples = ["lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
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+fabric-examples = ["lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.16.0,<1.0)"]
 fabric-strategies = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)"]
-fabric-test = ["click (==8.1.7)", "coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)"]
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-pytorch-examples = ["ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "requests (<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
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 pytorch-extra = ["bitsandbytes (>=0.42.0,<1.0)", "hydra-core (>=1.2.0,<2.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)"]
 pytorch-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
-pytorch-test = ["cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "uvicorn"]
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 [[package]]
 name = "lightning-utilities"
-version = "0.11.3.post0"
+version = "0.11.6"
 description = "Lightning toolbox for across the our ecosystem."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "lightning_utilities-0.11.6-py3-none-any.whl", hash = "sha256:ecd9953c316cbaf56ad820fbe7bd062187b9973c4a23d47b076cd59dc080a310"},
+    {file = "lightning_utilities-0.11.6.tar.gz", hash = "sha256:79fc27ef8ec8b8d55a537920f2c7610270c0c9e037fa6efc78f1aa34ec8cdf04"},
 ]
 
 [package.dependencies]
@@ -2607,153 +2651,149 @@ files = [
 
 [[package]]
 name = "lxml"
-version = "5.2.2"
+version = "5.3.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = false
 python-versions = ">=3.6"
 files = [
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 html-clean = ["lxml-html-clean"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=3.0.10)"]
+source = ["Cython (>=3.0.11)"]
 
 [[package]]
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@@ -2815,13 +2855,13 @@ testing = ["pytest"]
 
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 [package.dependencies]
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 [package.dependencies]
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     {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
@@ -3201,6 +3234,25 @@ files = [
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 ]
 
+[[package]]
+name = "narwhals"
+version = "1.5.2"
+description = "Extremely lightweight compatibility layer between dataframe libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+cudf = ["cudf (>=23.08.00)"]
+dask = ["dask[dataframe] (>=2024.7)"]
+modin = ["modin"]
+pandas = ["pandas (>=0.25.3)"]
+polars = ["polars (>=0.20.3)"]
+pyarrow = ["pyarrow (>=11.0.0)"]
+
 [[package]]
 name = "nbclient"
 version = "0.6.8"
@@ -3326,13 +3378,13 @@ pytest = ">=6.1.0"
 
 [[package]]
 name = "nbsphinx"
-version = "0.9.4"
+version = "0.9.5"
 description = "Jupyter Notebook Tools for Sphinx"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -3356,21 +3408,21 @@ files = [
 
 [[package]]
 name = "networkx"
-version = "2.8.8"
+version = "3.3"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 files = [
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 ]
 
 [package.extras]
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-developer = ["mypy (>=0.982)", "pre-commit (>=2.20)"]
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-test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
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+extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nfoursid"
@@ -3564,12 +3616,13 @@ files = [
 
 [[package]]
 name = "nvidia-cudnn-cu12"
-version = "8.9.2.26"
+version = "9.1.0.70"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [package.dependencies]
@@ -3640,13 +3693,14 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.5.82"
+version = "12.6.20"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -3853,8 +3907,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
     {version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
@@ -3887,12 +3941,12 @@ xml = ["lxml (>=4.8.0)"]
 
 [[package]]
 name = "pandoc"
-version = "2.3"
+version = "2.4"
 description = "Pandoc Documents for Python"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -4118,13 +4172,13 @@ type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "plotly"
-version = "5.22.0"
+version = "5.23.0"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "plotly-5.23.0.tar.gz", hash = "sha256:89e57d003a116303a34de6700862391367dd564222ab71f8531df70279fc0193"},
 ]
 
 [package.dependencies]
@@ -4243,13 +4297,13 @@ psutil = "*"
 
 [[package]]
 name = "pre-commit"
-version = "3.7.1"
+version = "3.8.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -4289,22 +4343,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "4.25.3"
+version = "4.25.4"
 description = ""
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4349,13 +4403,13 @@ files = [
 
 [[package]]
 name = "pure-eval"
-version = "0.2.2"
+version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 numpy = ">=1.16.6"
 
+[package.extras]
+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -4563,34 +4620,33 @@ files = [
 
 [[package]]
 name = "pytorch-lightning"
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+version = "2.4.0"
 description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
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 packaging = ">=20.0"
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-torch = ">=2.0.0"
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 torchmetrics = ">=0.7.0"
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 typing-extensions = ">=4.4.0"
 
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 [package.dependencies]
@@ -4940,110 +5019,114 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
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 ]
 
 [package.dependencies]
@@ -5132,8 +5223,8 @@ numpy = ">=1.23.5,<2.3"
 
 [package.extras]
 dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
-doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
-test = ["Cython", "array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "send2trash"
@@ -5153,18 +5244,19 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "70.2.0"
+version = "73.0.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
 
 [[package]]
 name = "shap"
@@ -5303,37 +5395,37 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.5"
+version = "2.6"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
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+    {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"},
 ]
 
 [[package]]
 name = "sphinx"
-version = "7.3.7"
+version = "7.4.7"
 description = "Python documentation generator"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinx-7.3.7-py3-none-any.whl", hash = "sha256:413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3"},
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+    {file = "sphinx-7.4.7-py3-none-any.whl", hash = "sha256:c2419e2135d11f1951cd994d6eb18a1835bd8fdd8429f9ca375dc1f3281bd239"},
+    {file = "sphinx-7.4.7.tar.gz", hash = "sha256:242f92a7ea7e6c5b406fdc2615413890ba9f699114a9c09192d7dfead2ee9cfe"},
 ]
 
 [package.dependencies]
 alabaster = ">=0.7.14,<0.8.0"
-babel = ">=2.9"
-colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.18.1,<0.22"
+babel = ">=2.13"
+colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\""}
+docutils = ">=0.20,<0.22"
 imagesize = ">=1.3"
-Jinja2 = ">=3.0"
-packaging = ">=21.0"
-Pygments = ">=2.14"
-requests = ">=2.25.0"
-snowballstemmer = ">=2.0"
+Jinja2 = ">=3.1"
+packaging = ">=23.0"
+Pygments = ">=2.17"
+requests = ">=2.30.0"
+snowballstemmer = ">=2.2"
 sphinxcontrib-applehelp = "*"
 sphinxcontrib-devhelp = "*"
 sphinxcontrib-htmlhelp = ">=2.0.0"
@@ -5344,8 +5436,8 @@ tomli = {version = ">=2", markers = "python_version < \"3.11\""}
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["flake8 (>=3.5.0)", "importlib_metadata", "mypy (==1.9.0)", "pytest (>=6.0)", "ruff (==0.3.7)", "sphinx-lint", "tomli", "types-docutils", "types-requests"]
-test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=6.0)", "setuptools (>=67.0)"]
+lint = ["flake8 (>=6.0)", "importlib-metadata (>=6.0)", "mypy (==1.10.1)", "pytest (>=6.0)", "ruff (==0.5.2)", "sphinx-lint (>=0.9)", "tomli (>=2)", "types-docutils (==0.21.0.20240711)", "types-requests (>=2.30.0)"]
+test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=8.0)", "setuptools (>=70.0)", "typing_extensions (>=4.9)"]
 
 [[package]]
 name = "sphinx-autobuild"
@@ -5368,49 +5460,49 @@ test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.8"
+version = "2.0.0"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_applehelp-1.0.8-py3-none-any.whl", hash = "sha256:cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4"},
-    {file = "sphinxcontrib_applehelp-1.0.8.tar.gz", hash = "sha256:c40a4f96f3776c4393d933412053962fac2b84f4c99a7982ba42e09576a70619"},
+    {file = "sphinxcontrib_applehelp-2.0.0-py3-none-any.whl", hash = "sha256:4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5"},
+    {file = "sphinxcontrib_applehelp-2.0.0.tar.gz", hash = "sha256:2f29ef331735ce958efa4734873f084941970894c6090408b079c61b2e1c06d1"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-devhelp"
-version = "1.0.6"
+version = "2.0.0"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_devhelp-1.0.6-py3-none-any.whl", hash = "sha256:6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f"},
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+    {file = "sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2"},
+    {file = "sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.5"
+version = "2.1.0"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_htmlhelp-2.0.5-py3-none-any.whl", hash = "sha256:393f04f112b4d2f53d93448d4bce35842f62b307ccdc549ec1585e950bc35e04"},
-    {file = "sphinxcontrib_htmlhelp-2.0.5.tar.gz", hash = "sha256:0dc87637d5de53dd5eec3a6a01753b1ccf99494bd756aafecd74b4fa9e729015"},
+    {file = "sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8"},
+    {file = "sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
@@ -5430,92 +5522,92 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.7"
+version = "2.0.0"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_qthelp-1.0.7-py3-none-any.whl", hash = "sha256:e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182"},
-    {file = "sphinxcontrib_qthelp-1.0.7.tar.gz", hash = "sha256:053dedc38823a80a7209a80860b16b722e9e0209e32fea98c90e4e6624588ed6"},
+    {file = "sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb"},
+    {file = "sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
-test = ["pytest"]
+test = ["defusedxml (>=0.7.1)", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.10"
+version = "2.0.0"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = false
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 ]
 
 [package.dependencies]
@@ -5568,18 +5660,18 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "statsforecast"
-version = "1.7.5"
+version = "1.7.6"
 description = "Time series forecasting suite using statistical models"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "statsforecast-1.7.6-py3-none-any.whl", hash = "sha256:a9d39ef0303788063562737912db79c84047177c9b2ac68a9c95c36d7b499028"},
+    {file = "statsforecast-1.7.6.tar.gz", hash = "sha256:fd956802e72039d9ac8833cfe693dbeef90c939132bf56984d312a2866fbd8ce"},
 ]
 
 [package.dependencies]
 cloudpickle = "*"
-coreforecast = ">=0.0.9"
+coreforecast = ">=0.0.12"
 fugue = ">=0.8.1"
 numba = ">=0.55.0"
 numpy = ">=1.21.6"
@@ -5591,11 +5683,12 @@ tqdm = "*"
 utilsforecast = ">=0.1.4"
 
 [package.extras]
+all = ["black", "datasetsforecast", "fire", "fugue[dask] (>=0.8.1)", "fugue[ray] (>=0.8.1)", "fugue[spark] (>=0.8.1)", "nbdev", "nbdev-plotly", "nbformat", "numpy (<2)", "pandas (<2.2)", "pandas[plot]", "plotly", "plotly-resampler", "pmdarima", "polars[numpy]", "pre-commit", "prophet", "protobuf (>=3.15.3,<4.0.0)", "pyarrow", "ray (<2.8)", "scikit-learn", "setuptools (<70)", "supersmoother"]
 dask = ["fugue[dask] (>=0.8.1)"]
-dev = ["black", "datasetsforecast", "fugue[dask] (>=0.8.1)", "fugue[ray] (>=0.8.1)", "fugue[spark] (>=0.8.1)", "mypy", "nbdev", "nbdev-plotly", "pandas[plot]", "plotly", "plotly-resampler", "pmdarima", "polars", "pre-commit", "prophet", "protobuf (>=3.15.3,<4.0.0)", "pyarrow", "ray (<2.8)", "ruff", "scikit-learn", "setuptools (<70)", "supersmoother"]
+dev = ["black", "datasetsforecast", "fire", "nbdev", "nbdev-plotly", "nbformat", "pandas[plot]", "pmdarima", "polars[numpy]", "pre-commit", "prophet", "pyarrow", "scikit-learn", "setuptools (<70)", "supersmoother"]
 plotly = ["plotly", "plotly-resampler"]
-polars = ["polars"]
-ray = ["fugue[ray] (>=0.8.1)", "protobuf (>=3.15.3,<4.0.0)", "ray (<2.8)"]
+polars = ["polars[numpy]"]
+ray = ["fugue[ray] (>=0.8.1)", "numpy (<2)", "pandas (<2.2)", "protobuf (>=3.15.3,<4.0.0)", "ray (<2.8)"]
 spark = ["fugue[spark] (>=0.8.1)"]
 
 [[package]]
@@ -5645,13 +5738,13 @@ docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "n
 
 [[package]]
 name = "sympy"
-version = "1.13.0"
+version = "1.13.2"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5680,28 +5773,15 @@ scipy = "*"
 [package.extras]
 dev = ["pip-tools", "pytest", "rpy2"]
 
-[[package]]
-name = "tbb"
-version = "2021.13.0"
-description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
-optional = false
-python-versions = "*"
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-]
-
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -5710,12 +5790,12 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tensorboard"
-version = "2.16.2"
+version = "2.17.1"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -5723,6 +5803,7 @@ absl-py = ">=0.4"
 grpcio = ">=1.48.2"
 markdown = ">=2.6.8"
 numpy = ">=1.12.0"
+packaging = "*"
 protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
 setuptools = ">=41.0.0"
 six = ">1.9"
@@ -5759,44 +5840,40 @@ protobuf = ">=3.20"
 
 [[package]]
 name = "tensorflow"
-version = "2.16.2"
+version = "2.17.0"
 description = "TensorFlow is an open source machine learning framework for everyone."
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
 absl-py = ">=1.0.0"
 astunparse = ">=1.6.0"
-flatbuffers = ">=23.5.26"
+flatbuffers = ">=24.3.25"
 gast = ">=0.2.1,<0.5.0 || >0.5.0,<0.5.1 || >0.5.1,<0.5.2 || >0.5.2"
 google-pasta = ">=0.1.1"
 grpcio = ">=1.24.3,<2.0"
 h5py = ">=3.10.0"
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+keras = ">=3.2.0"
 libclang = ">=13.0.0"
-ml-dtypes = ">=0.3.1,<0.4.0"
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 numpy = [
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     {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
@@ -5807,7 +5884,7 @@ protobuf = ">=3.20.3,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 requests = ">=2.21.0,<3"
 setuptools = "*"
 six = ">=1.12.0"
-tensorboard = ">=2.16,<2.17"
+tensorboard = ">=2.17,<2.18"
 tensorflow-io-gcs-filesystem = {version = ">=0.23.1", markers = "python_version < \"3.12\""}
 termcolor = ">=1.1.0"
 typing-extensions = ">=3.6.6"
@@ -5923,57 +6000,45 @@ files = [
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 ]
 
-[[package]]
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-version = "0.12.1"
-description = "List processing tools and functional utilities"
-optional = false
-python-versions = ">=3.7"
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-]
-
 [[package]]
 name = "torch"
-version = "2.3.1"
+version = "2.4.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
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 fsspec = "*"
 jinja2 = "*"
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 networkx = "*"
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 nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.9.1)"]
+optree = ["optree (>=0.11.0)"]
 
 [[package]]
 name = "torchmetrics"
-version = "1.4.0.post0"
+version = "1.4.1"
 description = "PyTorch native Metrics"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -6006,15 +6070,14 @@ packaging = ">17.1"
 torch = ">=1.10.0"
 
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-audio = ["pystoi (>=0.3.0)", "torchaudio (>=0.10.0)"]
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 visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.3.0)"]
 
 [[package]]
@@ -6039,13 +6102,13 @@ files = [
 
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-version = "4.66.4"
+version = "4.66.5"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -6096,17 +6159,21 @@ ciso8601 = ["ciso8601"]
 
 [[package]]
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 python-versions = "*"
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 [package.dependencies]
@@ -6114,18 +6181,18 @@ filelock = "*"
 
 [package.extras]
 build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
-tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20240316"
+version = "2.9.0.20240821"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
-    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
+    {file = "types-python-dateutil-2.9.0.20240821.tar.gz", hash = "sha256:9649d1dcb6fef1046fb18bebe9ea2aa0028b160918518c34589a46045f6ebd98"},
+    {file = "types_python_dateutil-2.9.0.20240821-py3-none-any.whl", hash = "sha256:f5889fcb4e63ed4aaa379b44f93c32593d50b9a94c9a60a0c854d8cc3511cd57"},
 ]
 
 [[package]]
@@ -6183,13 +6250,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "utilsforecast"
-version = "0.1.12"
+version = "0.2.3"
 description = "Forecasting utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "utilsforecast-0.1.12-py3-none-any.whl", hash = "sha256:728de990034b1fc27fe45be268e15154aa7bf32b62ab065646e11c4066da5e22"},
-    {file = "utilsforecast-0.1.12.tar.gz", hash = "sha256:04f065158b70a343d60e197dc7973f24a22b82254a1b660e3f9b950d08e611ff"},
+    {file = "utilsforecast-0.2.3-py3-none-any.whl", hash = "sha256:780c11c43aec0d506b314a97d21ce94ca2bd24c4ed8e1db5df2eff2c25989b10"},
+    {file = "utilsforecast-0.2.3.tar.gz", hash = "sha256:c48859a69428628f1787a963599fcf980f9403c57db4b251955707a01af00131"},
 ]
 
 [package.dependencies]
@@ -6198,10 +6265,9 @@ packaging = "*"
 pandas = ">=1.1.1"
 
 [package.extras]
-dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "numpy (<2)", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"]
+dev = ["datasetsforecast (==0.0.8)", "nbdev", "pandas[plot]", "plotly", "plotly-resampler", "polars[numpy]", "pyarrow", "scipy"]
 plotting = ["pandas[plot]", "plotly", "plotly-resampler"]
-polars = ["numpy (<2)", "polars"]
-scalers = ["numba", "scipy"]
+polars = ["polars[numpy]"]
 
 [[package]]
 name = "vega-datasets"
@@ -6277,16 +6343,17 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "vl-convert-python"
-version = "1.5.0"
+version = "1.6.0"
 description = "Convert Vega-Lite chart specifications to SVG, PNG, or Vega"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "vl_convert_python-1.5.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fba51e437a09aa0fb04e1931a4b851231053aeb14715a3653e272c829aac1dc4"},
-    {file = "vl_convert_python-1.5.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0244666476817c1f772d26286485c24ce2218fafd9b4f1913a6ef21857083531"},
-    {file = "vl_convert_python-1.5.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:889a403781e08a77e22370a6cac25e1c0720042dbaa7d006a5dcc3bf015e7587"},
-    {file = "vl_convert_python-1.5.0-cp37-abi3-win_amd64.whl", hash = "sha256:6d493952f568c85a445c2e515371a61f7ff2d09f5585e915abceec3efcd5826b"},
-    {file = "vl_convert_python-1.5.0.tar.gz", hash = "sha256:ba1e13a84f5a3d1af393d0d32aa19c4268db573d5f5c799bce8a5dc0468b1899"},
+    {file = "vl_convert_python-1.6.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:8c69676fa0dfb7b1af67364fd02304e5452ee6b873048d65fe4214f7cf726754"},
+    {file = "vl_convert_python-1.6.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:1c2ca0c2800925a1c6cf0cca4c79291205ceebabf1e9a40f90dd8bcead1af6eb"},
+    {file = "vl_convert_python-1.6.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d016d8d0be7a4ebb9dea447e0fe4373fb2998828ca05ae1b50c01d896edcece"},
+    {file = "vl_convert_python-1.6.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:92d51f8c2e6c4f4c18f0a0e3a79912fc4bb77dee542d9b62bcb05cd076007f44"},
+    {file = "vl_convert_python-1.6.0-cp37-abi3-win_amd64.whl", hash = "sha256:2167d762f77991a36ce11d47d03d399219e80a1f19340ed1f6125713f8b31e87"},
+    {file = "vl_convert_python-1.6.0.tar.gz", hash = "sha256:f7539c5558026d41d870df3ea92546ef87956c7b62ca99426906e941115bfd41"},
 ]
 
 [[package]]
@@ -6302,13 +6369,13 @@ files = [
 
 [[package]]
 name = "webcolors"
-version = "24.6.0"
+version = "24.8.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "webcolors-24.6.0-py3-none-any.whl", hash = "sha256:8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1"},
-    {file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"},
+    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
+    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
 ]
 
 [package.extras]
@@ -6344,13 +6411,13 @@ test = ["websockets"]
 
 [[package]]
 name = "werkzeug"
-version = "3.0.3"
+version = "3.0.4"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
-    {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
+    {file = "werkzeug-3.0.4-py3-none-any.whl", hash = "sha256:02c9eb92b7d6c06f31a782811505d2157837cea66aaede3e217c7c27c039476c"},
+    {file = "werkzeug-3.0.4.tar.gz", hash = "sha256:34f2371506b250df4d4f84bfe7b0921e4762525762bbd936614909fe25cd7306"},
 ]
 
 [package.dependencies]
@@ -6361,13 +6428,13 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "wheel"
-version = "0.43.0"
+version = "0.44.0"
 description = "A built-package format for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"},
-    {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"},
+    {file = "wheel-0.44.0-py3-none-any.whl", hash = "sha256:2376a90c98cc337d18623527a97c31797bd02bad0033d41547043a1cbfbe448f"},
+    {file = "wheel-0.44.0.tar.gz", hash = "sha256:a29c3f2817e95ab89aa4660681ad547c0e9547f20e75b0562fe7723c9a2a9d49"},
 ]
 
 [package.extras]
@@ -6465,13 +6532,13 @@ files = [
 
 [[package]]
 name = "xarray"
-version = "2024.6.0"
+version = "2024.7.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "xarray-2024.6.0-py3-none-any.whl", hash = "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"},
-    {file = "xarray-2024.6.0.tar.gz", hash = "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7"},
+    {file = "xarray-2024.7.0-py3-none-any.whl", hash = "sha256:1b0fd51ec408474aa1f4a355d75c00cc1c02bd425d97b2c2e551fd21810e7f64"},
+    {file = "xarray-2024.7.0.tar.gz", hash = "sha256:4cae512d121a8522d41e66d942fb06c526bc1fd32c2c181d5fe62fe65b671638"},
 ]
 
 [package.dependencies]
@@ -6489,19 +6556,19 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "xgboost"
-version = "2.1.0"
+version = "2.1.1"
 description = "XGBoost Python Package"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "xgboost-2.1.0-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl", hash = "sha256:19d145eb847b070c32342b1bf2d7331c102783e07a484f8b13b7d759d707c6b0"},
-    {file = "xgboost-2.1.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:840a0c6e2119d8c8f260a5dace996ea064a267f62b301a25d7d452488a7ac860"},
-    {file = "xgboost-2.1.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:73673c9bb85927db7fe2e3aed6df6d35dba708cfd6767cc63d4ea11dda2dede5"},
-    {file = "xgboost-2.1.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:43b16205689249d7509daf7a6ab00ad0e6c570b3a9c263cb32b26e39d9477bb3"},
-    {file = "xgboost-2.1.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:cedc2e386e686795735448fd4597533acacc5ba6fb47dd910c204c468b80bb96"},
-    {file = "xgboost-2.1.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:b2a456eb0f3d3e8fd8ab37e44ac288292bf8ea8744c294be9fd88713d27af810"},
-    {file = "xgboost-2.1.0-py3-none-win_amd64.whl", hash = "sha256:74904b91c42524a6c32147fe5718569e78fb65911ff4499b053f81d0964514d4"},
-    {file = "xgboost-2.1.0.tar.gz", hash = "sha256:7144980923e76ce741c7b03a14d3bd7514db6de5c7cabe96ba95b229d274f5ca"},
+    {file = "xgboost-2.1.1-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl", hash = "sha256:4163ab55118628f605cfccf950e2d667150640f6fc746bb5a173bddfd935950f"},
+    {file = "xgboost-2.1.1-py3-none-macosx_12_0_arm64.whl", hash = "sha256:40d1f647022f497c1b0f69073765baf50ff5802ca77c6bb1aca55a6bc65df00d"},
+    {file = "xgboost-2.1.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4c534818aa08ab327ac2239ef211ef78db65a8573d069bc9898f824830fa2308"},
+    {file = "xgboost-2.1.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:deef471e8d353afa99e5cc0e2af7d99ace7013f40684fcf3eed9124de033265d"},
+    {file = "xgboost-2.1.1-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:8f3246a6d839dceb4553d3e5ea64ed718f9c692f072ee8275eeb895b58e283e6"},
+    {file = "xgboost-2.1.1-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:6475ca35dede1f87d1dc485b362caba08f69f6020f4440e97b167676a533850e"},
+    {file = "xgboost-2.1.1-py3-none-win_amd64.whl", hash = "sha256:fcf8413f3c621e97fdaaa45abb7ae808319c88eff5447328eff14c419c7c6ae0"},
+    {file = "xgboost-2.1.1.tar.gz", hash = "sha256:4b1729837f9f1ba88a32ef1be3f8efb860fee6454a68719b196dc88032c23d97"},
 ]
 
 [package.dependencies]
@@ -6512,7 +6579,7 @@ scipy = "*"
 [package.extras]
 dask = ["dask", "distributed", "pandas"]
 datatable = ["datatable"]
-pandas = ["pandas"]
+pandas = ["pandas (>=1.2)"]
 plotting = ["graphviz", "matplotlib"]
 pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
 scikit-learn = ["scikit-learn"]
@@ -6623,4 +6690,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "265adeec0ea6039d404b2c7f4445cce187e0a9cebd0a0b04d62b2338bd7f96da"
+content-hash = "08403137a0efa3dbf7c52349dc2d5079ecb795fdc8e6c1d19c7c7b54713d70cf"
diff --git a/src/ontime/api/modular.py b/src/ontime/api/modular.py
index a1525c2..94d9b61 100644
--- a/src/ontime/api/modular.py
+++ b/src/ontime/api/modular.py
@@ -18,7 +18,16 @@
 
 """
 
-from ..core import detectors, generators, Model, models, Plot, marks, processors, TimeSeries
+from ..core import (
+    detectors,
+    generators,
+    Model,
+    models,
+    Plot,
+    marks,
+    processors,
+    TimeSeries,
+)
 
 from .. import module
 from .. import context
diff --git a/src/ontime/context/common/anomaly_frequency.py b/src/ontime/context/common/anomaly_frequency.py
index 3ccc54c..f38eed1 100644
--- a/src/ontime/context/common/anomaly_frequency.py
+++ b/src/ontime/context/common/anomaly_frequency.py
@@ -19,9 +19,7 @@ def get_number_of_anomaly_in_window(self, window_size: str) -> TimeSeries:
         sum_series = self.anomalies_series.rolling(window=window_size).sum()
         return TimeSeries.from_series(sum_series)
 
-    def get_frequency_of_anomaly_in_window(
-        self, window_size: str
-    ) -> UnitTimeSeries:
+    def get_frequency_of_anomaly_in_window(self, window_size: str) -> UnitTimeSeries:
         """
         Compute the frequency of anomalies in a time window. The frequency is computed as the number of anomalies in
         the window divided by the maximum number of anomalies in a window. So 1 mean that all samples in the window are
diff --git a/src/ontime/context/common/data_quality_detector.py b/src/ontime/context/common/data_quality_detector.py
index bf1faea..5277152 100644
--- a/src/ontime/context/common/data_quality_detector.py
+++ b/src/ontime/context/common/data_quality_detector.py
@@ -7,10 +7,12 @@ class DataQualityDetector:
     Detects anomalies in a time series given a threshold or quantile.
     """
 
-    def __init__(self,
-                 threshold_type: str,
-                 upper_threshold: float = None,
-                 lower_threshold: float = None):
+    def __init__(
+        self,
+        threshold_type: str,
+        upper_threshold: float = None,
+        lower_threshold: float = None,
+    ):
         """
         Constructor for the DataQualityDetector class.
 
@@ -30,20 +32,19 @@ def fit(self, ts: TimeSeries):
         :param ts: TimeSeries
         """
         match self.threshold_type:
-            case 'quantile':
+            case "quantile":
                 self.detector = Quantile(
                     low_quantile=self.lower_threshold,
-                    high_quantile=self.upper_threshold
+                    high_quantile=self.upper_threshold,
                 )
                 self.detector.fit(ts)
-            case 'threshold':
+            case "threshold":
                 self.detector = Threshold(
                     low_threshold=self.lower_threshold,
-                    high_threshold=self.upper_threshold
+                    high_threshold=self.upper_threshold,
                 )
         self.is_fitted = True
 
-
     def detect(self, ts: TimeSeries) -> BinaryTimeSeries:
         """
         Detects anomalies in the given time series given a quantile or threshold crossing.
diff --git a/src/ontime/context/common/missing_data_detector.py b/src/ontime/context/common/missing_data_detector.py
index 0a68f11..7d01a30 100644
--- a/src/ontime/context/common/missing_data_detector.py
+++ b/src/ontime/context/common/missing_data_detector.py
@@ -15,4 +15,3 @@ def detect(self, ts: TimeSeries) -> BinaryTimeSeries:
         :return: BinaryTimeSeries with 0 for normal values and 1 for anomalies
         """
         return BinaryTimeSeries.from_series(ts.pd_series().isna())
-
diff --git a/src/ontime/core/__init__.py b/src/ontime/core/__init__.py
index 229f4c5..2ef45a5 100644
--- a/src/ontime/core/__init__.py
+++ b/src/ontime/core/__init__.py
@@ -5,4 +5,13 @@
 from .processing import processors, abstract_processor
 from .time_series import TimeSeries
 
-__all__ = ["detectors", "generators", "Model", "models", "Plot", "marks", "processors", "TimeSeries"]
+__all__ = [
+    "detectors",
+    "generators",
+    "Model",
+    "models",
+    "Plot",
+    "marks",
+    "processors",
+    "TimeSeries",
+]
diff --git a/src/ontime/core/detection/registry/quantile.py b/src/ontime/core/detection/registry/quantile.py
index f20b415..93e60a7 100644
--- a/src/ontime/core/detection/registry/quantile.py
+++ b/src/ontime/core/detection/registry/quantile.py
@@ -11,11 +11,11 @@ class Quantile(QuantileDetector, AbstractDetector):
     """
 
     def __init__(
-            self,
-            low_quantile: float = None,
-            high_quantile: float = None,
-            enable_logging: bool = False,
-            logger_params: dict = None
+        self,
+        low_quantile: float = None,
+        high_quantile: float = None,
+        enable_logging: bool = False,
+        logger_params: dict = None,
     ):
         """
         Constructor for QuantileDetector
@@ -26,7 +26,7 @@ def __init__(
         """
         super().__init__(low_quantile, high_quantile)
         self.enable_logging = enable_logging
-        default_params = {'description': 'QuantileDetector'}
+        default_params = {"description": "QuantileDetector"}
         self.logger_params = default_params if logger_params is None else logger_params
         if enable_logging:
             self.logger = BinaryAnomalyLogger(**self.logger_params)
diff --git a/src/ontime/core/detection/registry/threshold.py b/src/ontime/core/detection/registry/threshold.py
index 37d9a43..82a1e29 100644
--- a/src/ontime/core/detection/registry/threshold.py
+++ b/src/ontime/core/detection/registry/threshold.py
@@ -13,11 +13,11 @@ class Threshold(ThresholdDetector, AbstractDetector):
     """
 
     def __init__(
-            self,
-            low_threshold: Union[int, float, None] = None,
-            high_threshold: Union[int, float, None] = None,
-            enable_logging: bool = False,
-            logger_params: dict = None
+        self,
+        low_threshold: Union[int, float, None] = None,
+        high_threshold: Union[int, float, None] = None,
+        enable_logging: bool = False,
+        logger_params: dict = None,
     ):
         """
         :param low_threshold: lower bounds
@@ -28,7 +28,7 @@ def __init__(
         """
         super().__init__(low_threshold, high_threshold)
         self.enable_logging = enable_logging
-        default_params = {'description': 'ThresholdDetector'}
+        default_params = {"description": "ThresholdDetector"}
         self.logger_params = default_params if logger_params is None else logger_params
         if enable_logging:
             self.logger = BinaryAnomalyLogger(**self.logger_params)
diff --git a/src/ontime/core/modelling/__init__.py b/src/ontime/core/modelling/__init__.py
index 20ae94e..2c8338e 100644
--- a/src/ontime/core/modelling/__init__.py
+++ b/src/ontime/core/modelling/__init__.py
@@ -1,12 +1,13 @@
 from .model import Model
 from .models import Models
 from .registry.gru import GRU
-#from .registry.autoencoder import Autoencoder
-#from .registry.variational_autoencoder import VariationalAutoencoder
+
+# from .registry.autoencoder import Autoencoder
+# from .registry.variational_autoencoder import VariationalAutoencoder
 
 models = Models()
 models.load("gru", GRU)
-#models.load("autoencoder", AutoEncoder)
-#models.load("variational_autoencoder", VariationalAutoencoder)
+# models.load("autoencoder", AutoEncoder)
+# models.load("variational_autoencoder", VariationalAutoencoder)
 
 __all__ = ["Model", "models"]
diff --git a/src/ontime/core/modelling/registry/autoencoder.py b/src/ontime/core/modelling/registry/autoencoder.py
index c1bcdee..111f694 100644
--- a/src/ontime/core/modelling/registry/autoencoder.py
+++ b/src/ontime/core/modelling/registry/autoencoder.py
@@ -14,7 +14,7 @@
 from ontime.module.benchmarking.benchmark import Benchmark
 
 
-device = 'cpu'
+device = "cpu"
 
 
 class Encoder(nn.Module):
@@ -64,7 +64,6 @@ def __str__(self):
 
 
 class Autoencoder(nn.Module):
-
     def __init__(self, entry_size: int, latent_dims: int):
         super(Autoencoder, self).__init__()
         self.encoder = Encoder(entry_size, latent_dims)
@@ -76,16 +75,18 @@ def forward(self, x: Tensor) -> Tensor:
         return self.decoder(z)
 
     def loss(self, x: Tensor, x_hat: Tensor) -> float:
-        loss = F.smooth_l1_loss(x, x_hat, reduction='mean')
+        loss = F.smooth_l1_loss(x, x_hat, reduction="mean")
         return loss
 
-    def get_reconstructed(self, dataset: TimeSeries, period: int, labels: TimeSeries = None, verbose: bool = False) -> \
-    list[list]:
+    def get_reconstructed(
+        self,
+        dataset: TimeSeries,
+        period: int,
+        labels: TimeSeries = None,
+        verbose: bool = False,
+    ) -> list[list]:
         ds = SlicedDataset(dataset, period, labels)
-        data = torch.utils.data.DataLoader(
-            ds,
-            batch_size=1,
-            shuffle=False)
+        data = torch.utils.data.DataLoader(ds, batch_size=1, shuffle=False)
         results_x = []
         results_xhat = []
         results_y = []
@@ -95,8 +96,8 @@ def get_reconstructed(self, dataset: TimeSeries, period: int, labels: TimeSeries
             x = x.to(device)  # CPU
             x_hat = self(x)
             if verbose:
-                print(f'x:{x.size()} -> x_hat {x_hat.size()}')
-            x_hat = x_hat.reshape(x.size()).to('cpu').detach()
+                print(f"x:{x.size()} -> x_hat {x_hat.size()}")
+            x_hat = x_hat.reshape(x.size()).to("cpu").detach()
             loss = self.loss(x, x_hat)
             x_hat = x_hat.numpy()
             x = x.cpu().numpy()
@@ -106,15 +107,17 @@ def get_reconstructed(self, dataset: TimeSeries, period: int, labels: TimeSeries
             results_loss.append(loss)
             if ds.labels is not None:
                 results_y.append(y)
-        reconstructed_dataset = Autoencoder._into_timeseries(dataset, results_xhat, results_loss, results_y, period)
+        reconstructed_dataset = Autoencoder._into_timeseries(
+            dataset, results_xhat, results_loss, results_y, period
+        )
         return reconstructed_dataset
 
     def train(self, data: SlicedDataset, device: str, epochs: int = 20):
         opt = torch.optim.Adam(self.parameters())
         for epoch in range(epochs):
-            i=1
+            i = 1
             for x, y in data:
-                i+=1
+                i += 1
                 x = x.to(device)  # CPU
                 opt.zero_grad()
                 x_hat = self(x)
@@ -127,18 +130,26 @@ def train(self, data: SlicedDataset, device: str, epochs: int = 20):
     def get_period(dataset: TimeSeries):
         periods = []
         for col in dataset.columns:
-            periods.append(pyd.findfrequency(dataset.pd_dataframe()[col].to_numpy(), detrend=True))
+            periods.append(
+                pyd.findfrequency(dataset.pd_dataframe()[col].to_numpy(), detrend=True)
+            )
         period = max(periods)
         while period < 15:
             period += period
         return period
 
     @staticmethod
-    def new_encoder_for_dataset(dataset: TimeSeries, period: int = None) -> 'Autoencoder':
+    def new_encoder_for_dataset(
+        dataset: TimeSeries, period: int = None
+    ) -> "Autoencoder":
         if period is None:
             periods = []
             for col in dataset.columns:
-                periods.append(pyd.findfrequency(dataset.pd_dataframe()[col].to_numpy(), detrend=True))
+                periods.append(
+                    pyd.findfrequency(
+                        dataset.pd_dataframe()[col].to_numpy(), detrend=True
+                    )
+                )
             period = max(periods)
             while period < 15:
                 period += period
@@ -157,8 +168,12 @@ def _into_timeseries(input_dataset, xhat, loss, y, period):
             sample = xhat[i][0]
             for line in sample:
                 x_hat_flat.append(line)
-        reconstructed_dataset = pd.DataFrame(x_hat_flat, columns=input_dataset.columns.tolist())
-        reconstructed_dataset.index = input_dataset.time_index[:len(reconstructed_dataset.index)]
+        reconstructed_dataset = pd.DataFrame(
+            x_hat_flat, columns=input_dataset.columns.tolist()
+        )
+        reconstructed_dataset.index = input_dataset.time_index[
+            : len(reconstructed_dataset.index)
+        ]
 
         # loss and y
         reconstructed_loss = []
@@ -166,12 +181,12 @@ def _into_timeseries(input_dataset, xhat, loss, y, period):
         for l in loss:
             for i in range(0, period):
                 reconstructed_loss.append(l)
-        reconstructed_dataset['loss'] = reconstructed_loss
+        reconstructed_dataset["loss"] = reconstructed_loss
 
         if y is not None and len(y) > 0:
             for ry in y:
                 for i in range(0, period):
                     reconstructed_y.append(ry)
-            reconstructed_dataset['y'] = reconstructed_y
+            reconstructed_dataset["y"] = reconstructed_y
 
         return TimeSeries.from_pandas(reconstructed_dataset)
diff --git a/src/ontime/core/modelling/registry/gru.py b/src/ontime/core/modelling/registry/gru.py
index b155cf0..caf7a4f 100644
--- a/src/ontime/core/modelling/registry/gru.py
+++ b/src/ontime/core/modelling/registry/gru.py
@@ -2,13 +2,20 @@
 from torch import nn
 import pytorch_lightning as pl
 
-from ontime.core.modelling.libs.pytorch.abstract_pytorch_model import AbstractPytorchModel
+from ontime.core.modelling.libs.pytorch.abstract_pytorch_model import (
+    AbstractPytorchModel,
+)
 
 
 class GRU(AbstractPytorchModel):
     def __init__(self, input_dim, hidden_dim, output_steps, num_layers=1):
         super(GRU, self).__init__()
-        self.gru = nn.GRU(input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True)
+        self.gru = nn.GRU(
+            input_size=input_dim,
+            hidden_size=hidden_dim,
+            num_layers=num_layers,
+            batch_first=True,
+        )
         self.linear = nn.Linear(hidden_dim, output_steps)
 
     def forward(self, x):
@@ -20,7 +27,7 @@ def training_step(self, batch, batch_idx):
         x, y = batch
         y_hat = self(x)
         loss = nn.functional.mse_loss(y_hat, y)
-        self.log('train_loss', loss)
+        self.log("train_loss", loss)
         return loss
 
     def configure_optimizers(self):
@@ -39,8 +46,9 @@ def predict(self, horizon, *args, **kwargs):
         with torch.no_grad():
             return self(dummy_input)
 
+
 # Example usage
 # model = TimeSeriesGRU(input_dim=10, hidden_dim=20, output_steps=5)
 # ts_loader = YourDataLoader(time_series)
 # model.fit(ts_loader)
-# predictions = model.predict(horizon=5)
\ No newline at end of file
+# predictions = model.predict(horizon=5)
diff --git a/src/ontime/core/modelling/registry/variational_autoencoder.py b/src/ontime/core/modelling/registry/variational_autoencoder.py
index 1af779d..db296a6 100644
--- a/src/ontime/core/modelling/registry/variational_autoencoder.py
+++ b/src/ontime/core/modelling/registry/variational_autoencoder.py
@@ -6,31 +6,31 @@
 from .autoencoder import Encoder, Decoder, Autoencoder
 from ontime.core.time_series import TimeSeries
 
+
 class VariationalEncoder(Encoder):
     def __init__(self, entry_size: int, latent_dims: int):
         super(VariationalEncoder, self).__init__(entry_size, latent_dims)
         self.insize = entry_size
-        self.midsize = latent_dims + (entry_size - latent_dims)//2
+        self.midsize = latent_dims + (entry_size - latent_dims) // 2
         self.outsize = latent_dims
         self.linear1 = nn.Linear(entry_size, self.midsize)
         self.linear2 = nn.Linear(self.midsize, latent_dims)
         self.linear3 = nn.Linear(self.midsize, latent_dims)
 
         self.N = torch.distributions.Normal(0, 1)
-        #self.N.loc = self.N.loc.cuda() # hack to get sampling on the GPU
-        #self.N.scale = self.N.scale.cuda()
+        # self.N.loc = self.N.loc.cuda() # hack to get sampling on the GPU
+        # self.N.scale = self.N.scale.cuda()
         self.kl = 0
         self.double()
-        
+
     def forward(self, x: Tensor) -> Tensor:
         x = torch.flatten(x, start_dim=1)
         x = F.relu(self.linear1(x))
         mu = self.linear2(x)
         sigma = torch.exp(self.linear3(x))
-        z = mu + sigma*self.N.sample(mu.shape)
-        self.kl = (sigma**2 + mu**2 - torch.log(sigma) - 1/2).sum()
+        z = mu + sigma * self.N.sample(mu.shape)
+        self.kl = (sigma**2 + mu**2 - torch.log(sigma) - 1 / 2).sum()
         return z
-    
 
     def __str__(self):
         s = f"Encoder: 3 layers\n\
@@ -38,7 +38,8 @@ def __str__(self):
                 layer 1(mu): {self.midsize} -> {self.outsize}\n\
                 layer 2(sigma): {self.midsize} -> {self.outsize}"
         return s
-    
+
+
 class VariationalAutoencoder(Autoencoder):
     def __init__(self, entry_size: int, latent_dims: int):
         super(VariationalAutoencoder, self).__init__(entry_size, latent_dims)
@@ -49,17 +50,19 @@ def __init__(self, entry_size: int, latent_dims: int):
     def forward(self, x: Tensor) -> Tensor:
         z = self.encoder(x)
         return self.decoder(z)
-    
+
     def loss(self, x, x_hat):
-        return ((x - x_hat)**2).sum() + self.encoder.kl
-    
-    #train method should be correctly inherited from AutoEncoder
-    
+        return ((x - x_hat) ** 2).sum() + self.encoder.kl
+
+    # train method should be correctly inherited from AutoEncoder
+
     @staticmethod
-    def new_encoder_for_dataset(dataset: TimeSeries, period) -> 'VariationalAutoencoder':
-        entry_size = len(dataset.columns.tolist())*period
-        latent_dims = entry_size//4 # arbitrary choice
+    def new_encoder_for_dataset(
+        dataset: TimeSeries, period
+    ) -> "VariationalAutoencoder":
+        entry_size = len(dataset.columns.tolist()) * period
+        latent_dims = entry_size // 4  # arbitrary choice
         return VariationalAutoencoder(entry_size, latent_dims)
 
     def __str__(self):
-        return str(self.encoder) + "\n" + str(self.decoder)
\ No newline at end of file
+        return str(self.encoder) + "\n" + str(self.decoder)
diff --git a/src/ontime/core/plotting/_marks/activity.py b/src/ontime/core/plotting/_marks/activity.py
index f0874ef..d4acf00 100644
--- a/src/ontime/core/plotting/_marks/activity.py
+++ b/src/ontime/core/plotting/_marks/activity.py
@@ -4,7 +4,9 @@
 from ...time_series import TimeSeries
 
 
-def activity(ts: TimeSeries, resampling_method='sum', cmap='YlGn', linewidth=2, figsize=(12, 10)) -> tuple:
+def activity(
+    ts: TimeSeries, resampling_method="sum", cmap="YlGn", linewidth=2, figsize=(12, 10)
+) -> tuple:
     """
     Plot the activity of a TimeSeries in the style of a GitHub activity plot
 
@@ -23,10 +25,10 @@ def activity(ts: TimeSeries, resampling_method='sum', cmap='YlGn', linewidth=2,
         cmap=cmap,
         how=resampling_method,
         linewidth=linewidth,
-        fillcolor='#ADADAD',
-        ax=ax
+        fillcolor="#ADADAD",
+        ax=ax,
     )
 
     plt.gca().grid(False)
     plt.show()
-    return fig, ax
\ No newline at end of file
+    return fig, ax
diff --git a/src/ontime/core/plotting/_marks/area.py b/src/ontime/core/plotting/_marks/area.py
index 3219c68..8138adc 100644
--- a/src/ontime/core/plotting/_marks/area.py
+++ b/src/ontime/core/plotting/_marks/area.py
@@ -5,72 +5,64 @@
 from ..plot import Plot
 
 
-def area(ts: TimeSeries,
-         title: str = 'Area',
-         encode_kwargs: dict = None
-         ) -> Chart:
-
+def area(ts: TimeSeries, title: str = "Area", encode_kwargs: dict = None) -> Chart:
     # Define type given the number of components
     if ts.is_univariate:
-        type = 'single'
+        type = "single"
     elif len(ts.components) == 2:
-        type = 'double'
+        type = "double"
     else:
         raise ValueError("TimeSeries must be univariate or have exactly two components")
 
     df = Plot.melt(ts)
 
     match type:
-
-        case 'single':
+        case "single":
             assert ts.is_univariate, "TimeSeries must be univariate"
 
             # Manually add the title
-            df['area'] = title
+            df["area"] = title
 
             # Define kwargs
             default_kwargs = {
                 "x": "time:T",
                 "y": "value:Q",
-                "color": alt.Color('area:N', legend=alt.Legend(title="variable"))
+                "color": alt.Color("area:N", legend=alt.Legend(title="variable")),
             }
-            encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
+            encode_kwargs = (
+                encode_kwargs if encode_kwargs is not None else default_kwargs
+            )
 
             # Make the chart
             chart = (
-                Chart(df)
-                .mark_area(
-                    opacity=0.6,
-                    color='gray'
-                )
-                .encode(**encode_kwargs)
+                Chart(df).mark_area(opacity=0.6, color="gray").encode(**encode_kwargs)
             )
 
-        case 'double':
-            assert len(ts.components) == 2, "TimeSeries must have exactly two components"
+        case "double":
+            assert (
+                len(ts.components) == 2
+            ), "TimeSeries must have exactly two components"
 
             # Pivot the data and manually add the title
-            df = df.pivot(index='time', columns='variable', values='value').reset_index()
-            df['area'] = title
+            df = df.pivot(
+                index="time", columns="variable", values="value"
+            ).reset_index()
+            df["area"] = title
 
             # Define kwargs
             default_kwargs = {
                 "x": "time:T",
                 "y": f"{ts.columns[0]}:Q",
                 "y2": f"{ts.columns[1]}:Q",
-                "color": alt.Color('area:N', legend=alt.Legend(title="variable"))
+                "color": alt.Color("area:N", legend=alt.Legend(title="variable")),
             }
-            encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
+            encode_kwargs = (
+                encode_kwargs if encode_kwargs is not None else default_kwargs
+            )
 
             # Make the chart
             chart = (
-                Chart(df)
-                .mark_area(
-                    opacity=0.6,
-                    color='gray'
-                )
-                .encode(**encode_kwargs)
+                Chart(df).mark_area(opacity=0.6, color="gray").encode(**encode_kwargs)
             )
 
     return chart
-
diff --git a/src/ontime/core/plotting/_marks/dots.py b/src/ontime/core/plotting/_marks/dots.py
index b87c165..3545d48 100644
--- a/src/ontime/core/plotting/_marks/dots.py
+++ b/src/ontime/core/plotting/_marks/dots.py
@@ -4,9 +4,7 @@
 from ..plot import Plot
 
 
-def dots(ts: TimeSeries,
-         type: str = None,
-         encode_kwargs: dict = None) -> Chart:
+def dots(ts: TimeSeries, type: str = None, encode_kwargs: dict = None) -> Chart:
     """
     Plot dots of for TimeSeries
 
@@ -25,29 +23,16 @@ def dots(ts: TimeSeries,
     encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
 
     match type:
-
         # Dots (filled circle)
-        case 'circle':
-            chart = (
-                Chart(df)
-                .mark_circle(filled=False)
-                .encode(**encode_kwargs)
-            )
+        case "circle":
+            chart = Chart(df).mark_circle(filled=False).encode(**encode_kwargs)
 
         # Square
-        case 'square':
-            chart = (
-                Chart(df)
-                .mark_square()
-                .encode(**encode_kwargs)
-            )
+        case "square":
+            chart = Chart(df).mark_square().encode(**encode_kwargs)
 
         # Default dots (filled circle)
         case _:
-            chart = (
-                Chart(df)
-                .mark_circle()
-                .encode(**encode_kwargs)
-            )
+            chart = Chart(df).mark_circle().encode(**encode_kwargs)
 
     return chart
diff --git a/src/ontime/core/plotting/_marks/heatmap.py b/src/ontime/core/plotting/_marks/heatmap.py
index 405731f..3ee4884 100644
--- a/src/ontime/core/plotting/_marks/heatmap.py
+++ b/src/ontime/core/plotting/_marks/heatmap.py
@@ -35,7 +35,7 @@ def heatmap(ts: TimeSeries) -> Chart:
             ),
             alt.Y("variable:N").title(None),
             alt.Color("sum(value)").title("Value"),
-            )
+        )
     )
 
-    return chart
\ No newline at end of file
+    return chart
diff --git a/src/ontime/core/plotting/_marks/line.py b/src/ontime/core/plotting/_marks/line.py
index 8c53473..6fa8daa 100644
--- a/src/ontime/core/plotting/_marks/line.py
+++ b/src/ontime/core/plotting/_marks/line.py
@@ -4,9 +4,7 @@
 from ..plot import Plot
 
 
-def line(ts: TimeSeries,
-         type: str = None,
-         encode_kwargs: dict = None) -> Chart:
+def line(ts: TimeSeries, type: str = None, encode_kwargs: dict = None) -> Chart:
     """
     Line plot for TimeSeries
 
@@ -26,9 +24,8 @@ def line(ts: TimeSeries,
     encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
 
     match type:
-
         # Dashed line
-        case 'dashed':
+        case "dashed":
             chart = (
                 Chart(df)
                 .mark_line(
@@ -40,10 +37,6 @@ def line(ts: TimeSeries,
 
         # Default line
         case _:
-            chart = (
-                Chart(df)
-                .mark_line()
-                .encode(**encode_kwargs)
-            )
+            chart = Chart(df).mark_line().encode(**encode_kwargs)
 
     return chart
diff --git a/src/ontime/core/plotting/_marks/mark.py b/src/ontime/core/plotting/_marks/mark.py
index 9fda217..0197977 100644
--- a/src/ontime/core/plotting/_marks/mark.py
+++ b/src/ontime/core/plotting/_marks/mark.py
@@ -6,10 +6,9 @@
 from ..plot import Plot
 
 
-def mark(ts: TimeSeries,
-         data: TimeSeries,
-         type: str = None,
-         encode_kwargs: dict = None) -> Chart:
+def mark(
+    ts: TimeSeries, data: TimeSeries, type: str = None, encode_kwargs: dict = None
+) -> Chart:
     """
     Create a plot with marks on it
 
@@ -24,9 +23,9 @@ def get_marked_ts(ts, data) -> TimeSeries:
         Internal function to merge two TimeSeries
         where one is the original and the other is
         a binary mask.
-        
+
         This is useful for dots or highlights
-        
+
         :param ts: TimeSeries
         :param data: BinaryTimeSeries
         :return: TimeSeries
@@ -47,14 +46,16 @@ def get_marked_ranges(data) -> pd.DataFrame:
         :return:
         """
         df = Plot.melt(data)
-        df['mark_start'] = (df['value'] == 1) & (df['value'].shift(1) != 1)
-        df['mark_end'] = (df['value'] == 1) & (df['value'].shift(-1) != 1)
-        mark_ranges = pd.DataFrame({
-            'start': df.loc[df['mark_start'], 'time'],
-            'end': df.loc[df['mark_end'], 'time']
-        })
-        mark_ranges['start'] = mark_ranges['start'].ffill()
-        mark_ranges['end'] = mark_ranges['end'].bfill()
+        df["mark_start"] = (df["value"] == 1) & (df["value"].shift(1) != 1)
+        df["mark_end"] = (df["value"] == 1) & (df["value"].shift(-1) != 1)
+        mark_ranges = pd.DataFrame(
+            {
+                "start": df.loc[df["mark_start"], "time"],
+                "end": df.loc[df["mark_end"], "time"],
+            }
+        )
+        mark_ranges["start"] = mark_ranges["start"].ffill()
+        mark_ranges["end"] = mark_ranges["end"].bfill()
         mark_ranges = mark_ranges.drop_duplicates()
         return mark_ranges
 
@@ -68,39 +69,35 @@ def get_marked_ranges(data) -> pd.DataFrame:
     }
 
     match type:
-
-        case 'dot':
-            encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
+        case "dot":
+            encode_kwargs = (
+                encode_kwargs if encode_kwargs is not None else default_kwargs
+            )
             ts = get_marked_ts(ts, data)
             df = Plot.melt(ts)
-            chart = (
-                Chart(df)
-                .mark_circle()
-                .encode(**encode_kwargs)
-            )
+            chart = Chart(df).mark_circle().encode(**encode_kwargs)
 
-        case 'highlight':
-            encode_kwargs = encode_kwargs if encode_kwargs is not None else default_kwargs
+        case "highlight":
+            encode_kwargs = (
+                encode_kwargs if encode_kwargs is not None else default_kwargs
+            )
             ts = get_marked_ts(ts, data)
             df = Plot.melt(ts)
             chart = (
-                Chart(df)
-                .mark_line(
-                    strokeWidth=5,
-                    opacity=0.5
-                )
-                .encode(**encode_kwargs)
+                Chart(df).mark_line(strokeWidth=5, opacity=0.5).encode(**encode_kwargs)
             )
 
-        case 'background':
+        case "background":
             background_kwargs = {
                 "x": "start:T",
                 "x2": "end:T",
                 "color": "variable:N",
             }
-            encode_kwargs = encode_kwargs if encode_kwargs is not None else background_kwargs
+            encode_kwargs = (
+                encode_kwargs if encode_kwargs is not None else background_kwargs
+            )
             df = get_marked_ranges(data)
-            df['variable'] = data.columns[0]
+            df["variable"] = data.columns[0]
             chart = (
                 Chart(df)
                 .mark_rect(
diff --git a/src/ontime/core/plotting/marks.py b/src/ontime/core/plotting/marks.py
index e284c28..3fd2c17 100644
--- a/src/ontime/core/plotting/marks.py
+++ b/src/ontime/core/plotting/marks.py
@@ -7,11 +7,13 @@
 from ._marks.mark import mark
 
 # Export all marks in a DotDict
-marks = DotDict({
-    'activity': activity,
-    'area': area,
-    'dots': dots,
-    'heatmap': heatmap,
-    'line': line,
-    'mark': mark
-})
\ No newline at end of file
+marks = DotDict(
+    {
+        "activity": activity,
+        "area": area,
+        "dots": dots,
+        "heatmap": heatmap,
+        "line": line,
+        "mark": mark,
+    }
+)
diff --git a/src/ontime/core/plotting/plot.py b/src/ontime/core/plotting/plot.py
index 0304319..e13c8c9 100644
--- a/src/ontime/core/plotting/plot.py
+++ b/src/ontime/core/plotting/plot.py
@@ -7,17 +7,13 @@
 
 
 class Plot:
-
     def __init__(self, ts: TimeSeries = None, **kwargs):
         super().__init__(**kwargs)
         Plot.config()
         self.ts = ts
         self.layers = []
 
-    def add(self,
-            mark: Callable,
-            ts: TimeSeries = None,
-            **kwargs):
+    def add(self, mark: Callable, ts: TimeSeries = None, **kwargs):
         """
         Add a mark to the plot
 
@@ -69,4 +65,3 @@ def config():
         :return: None
         """
         alt.data_transformers.enable("vegafusion")
-
diff --git a/src/ontime/core/time_series/binary_time_series.py b/src/ontime/core/time_series/binary_time_series.py
index 6940f77..3e2d513 100644
--- a/src/ontime/core/time_series/binary_time_series.py
+++ b/src/ontime/core/time_series/binary_time_series.py
@@ -9,6 +9,7 @@ class BinaryTimeSeries(RestrictedTimeSeries):
     """
     A time series with restrictions on the data so that all values are either 0 or 1.
     """
+
     def __init__(self, xa: xr.DataArray):
         """
         Initialize the BinaryTimeSeries.
diff --git a/src/ontime/core/time_series/restricted_time_series.py b/src/ontime/core/time_series/restricted_time_series.py
index 04dddfd..f067b04 100644
--- a/src/ontime/core/time_series/restricted_time_series.py
+++ b/src/ontime/core/time_series/restricted_time_series.py
@@ -21,7 +21,9 @@ def __init__(self, xa: xr.DataArray, restrictions=None):
         self.restrictions = []
         if restrictions is not None:
             for r in restrictions:
-                assert isinstance(r, Restriction), "All restrictions must be of type Restriction"
+                assert isinstance(
+                    r, Restriction
+                ), "All restrictions must be of type Restriction"
                 self.add_restriction(r)
 
     def check(self) -> bool:
diff --git a/src/ontime/core/time_series/time_series.py b/src/ontime/core/time_series/time_series.py
index f6d1090..d0cb739 100644
--- a/src/ontime/core/time_series/time_series.py
+++ b/src/ontime/core/time_series/time_series.py
@@ -27,10 +27,10 @@ def plot(self, width: int = 400, height: int = 200, **kwargs):
         """
         from ..plotting.plot import Plot
         from ..plotting._marks.line import line
-        return Plot(self)\
-            .add(line, **kwargs)\
-            .properties(width=width, height=height)\
-            .show()
+
+        return (
+            Plot(self).add(line, **kwargs).properties(width=width, height=height).show()
+        )
 
     def rename(self, columns: dict):
         """
@@ -40,7 +40,9 @@ def rename(self, columns: dict):
         """
         col_names = list(columns.keys())
         col_names_new = list(columns.values())
-        return self.with_columns_renamed(col_names=col_names, col_names_new=col_names_new)
+        return self.with_columns_renamed(
+            col_names=col_names, col_names_new=col_names_new
+        )
 
     def split_by_period(self, period: str) -> List[TimeSeries]:
         """
diff --git a/src/ontime/core/utils/anomaly_logger.py b/src/ontime/core/utils/anomaly_logger.py
index 69e9a78..4c97032 100644
--- a/src/ontime/core/utils/anomaly_logger.py
+++ b/src/ontime/core/utils/anomaly_logger.py
@@ -16,13 +16,13 @@ def __init__(self, description: str, max_entries: int = 1000):
         """
         self.description = description
         self.max_entries = max_entries
-        self.log = pd.DataFrame(columns=['Timestamp', 'Description', 'Value'])
+        self.log = pd.DataFrame(columns=["Timestamp", "Description", "Value"])
 
     def reset_log(self):
         """
         Reset the log
         """
-        self.log = pd.DataFrame(columns=['Timestamp', 'Description', 'Value'])
+        self.log = pd.DataFrame(columns=["Timestamp", "Description", "Value"])
 
     def log_anomalies(self, ts: BinaryTimeSeries) -> pd.DataFrame:
         """
@@ -35,17 +35,19 @@ def log_anomalies(self, ts: BinaryTimeSeries) -> pd.DataFrame:
         df = ts.pd_dataframe()
         df = df.reset_index()
         print(df.columns)
-        log_df = pd.DataFrame({
-            'Timestamp': df.iloc[:, 0],
-            'Description': self.description,
-            'Value': df.iloc[:, 1] == 1
-        })
+        log_df = pd.DataFrame(
+            {
+                "Timestamp": df.iloc[:, 0],
+                "Description": self.description,
+                "Value": df.iloc[:, 1] == 1,
+            }
+        )
         # Merge w/ existing log
         self.log = pd.concat([self.log, log_df])
         # Sort by timestamp
-        self.log.sort_values(by='Timestamp', inplace=True)
+        self.log.sort_values(by="Timestamp", inplace=True)
         # Remove duplicates
-        self.log.drop_duplicates(subset='Timestamp', keep='last', inplace=True)
+        self.log.drop_duplicates(subset="Timestamp", keep="last", inplace=True)
         # Trim the log
         if self.max_entries:
             self.log = self.log.tail(self.max_entries)
diff --git a/src/ontime/core/utils/restriction.py b/src/ontime/core/utils/restriction.py
index 93d7de2..1861436 100644
--- a/src/ontime/core/utils/restriction.py
+++ b/src/ontime/core/utils/restriction.py
@@ -4,7 +4,6 @@
 
 
 class Restriction:
-
     def __init__(self, name: str, restriction: Callable):
         self.name = name
         self.restriction = restriction
diff --git a/src/ontime/module/benchmarking/benchmark.py b/src/ontime/module/benchmarking/benchmark.py
index 5066b60..0675c2e 100644
--- a/src/ontime/module/benchmarking/benchmark.py
+++ b/src/ontime/module/benchmarking/benchmark.py
@@ -1,6 +1,6 @@
 import sys
 
-sys.path.insert(0, '../../..')
+sys.path.insert(0, "../../..")
 import numpy as np
 from ontime.core.time_series.time_series import TimeSeries
 import pandas as pd
@@ -12,8 +12,15 @@
 class Benchmark:
     # a wrapper for datasets used in Benchmark
     class BenchmarkDataset:
-        def __init__(self, training_set: TimeSeries, multivariate: bool = None, name: str = None,
-                     test_set: TimeSeries = None, target_columns=None, *args):
+        def __init__(
+            self,
+            training_set: TimeSeries,
+            multivariate: bool = None,
+            name: str = None,
+            test_set: TimeSeries = None,
+            target_columns=None,
+            *args,
+        ):
             self.training_set = training_set
             self.test_set = test_set
             if multivariate is None:
@@ -34,8 +41,7 @@ def get_train_test_split(self, train_proportion=0.7):
     # a wrapper containing models used in Benchmark.
     # can be used as is or overridden to adapt for particular models
     class BenchmarkModelHolder:
-        def __init__(self, model_class, name: str,
-                     arguments_dict: dict = None):
+        def __init__(self, model_class, name: str, arguments_dict: dict = None):
             self.model = model_class
             self.model_instance = None
             self.args = arguments_dict
@@ -44,7 +50,14 @@ def __init__(self, model_class, name: str,
         def instantiate(self, train_set: TimeSeries, test_set: TimeSeries, **kwargs):
             self.model_instance = self.model(**self.args, **kwargs)
 
-        def fit(self, training_set: TimeSeries, test_set: TimeSeries, target_column=None, multivariate=False, **kwargs):
+        def fit(
+            self,
+            training_set: TimeSeries,
+            test_set: TimeSeries,
+            target_column=None,
+            multivariate=False,
+            **kwargs,
+        ):
             if multivariate:
                 if target_column is None:
                     target_column = training_set.columns.tolist()[0]
@@ -54,7 +67,14 @@ def fit(self, training_set: TimeSeries, test_set: TimeSeries, target_column=None
             else:
                 self.model_instance.fit(training_set, **kwargs)
 
-        def predict(self, horizon, test_set: TimeSeries, target_column=None, multivariate=False, **kwargs):
+        def predict(
+            self,
+            horizon,
+            test_set: TimeSeries,
+            target_column=None,
+            multivariate=False,
+            **kwargs,
+        ):
             if multivariate:
                 if target_column is None:
                     target_column = test_set.columns.tolist()[0]
@@ -74,12 +94,15 @@ def __init__(self, metric_function, name: str):
         def compute(self, data: TimeSeries, pred: TimeSeries):
             return self.metric(data, pred)
 
-    def __init__(self,
-                 datasets: List[Union[TimeSeries, Tuple[TimeSeries, TimeSeries], BenchmarkDataset]] = None,
-                 models: List[BenchmarkModelHolder] = None,
-                 metrics: List[BenchmarkMetric] = None,
-                 train_proportion=0.7):
-
+    def __init__(
+        self,
+        datasets: List[
+            Union[TimeSeries, Tuple[TimeSeries, TimeSeries], BenchmarkDataset]
+        ] = None,
+        models: List[BenchmarkModelHolder] = None,
+        metrics: List[BenchmarkMetric] = None,
+        train_proportion=0.7,
+    ):
         self.train_proportion = train_proportion
         # contains BenchmarkDatasets
         self.datasets = []
@@ -106,33 +129,58 @@ def __init__(self,
 
     def add_model(self, model):
         if not isinstance(model, Benchmark.BenchmarkModelHolder):
-            raise TypeError(f"models must implement {Benchmark.BenchmarkModelHolder}, found{type(model)}.")
+            raise TypeError(
+                f"models must implement {Benchmark.BenchmarkModelHolder}, found{type(model)}."
+            )
         self.models.append(model)
 
     def get_models(self):
         return [m.model_class for m in self.models]
 
-    def add_dataset(self, dataset: Union[TimeSeries, Tuple[TimeSeries, TimeSeries], BenchmarkDataset],
-                    target_columns=None, name=None):
-        if not (isinstance(dataset, TimeSeries) or
-                isinstance(dataset, Tuple) or
-                isinstance(dataset, Benchmark.BenchmarkDataset)):
+    def add_dataset(
+        self,
+        dataset: Union[TimeSeries, Tuple[TimeSeries, TimeSeries], BenchmarkDataset],
+        target_columns=None,
+        name=None,
+    ):
+        if not (
+            isinstance(dataset, TimeSeries)
+            or isinstance(dataset, Tuple)
+            or isinstance(dataset, Benchmark.BenchmarkDataset)
+        ):
             raise TypeError(
                 f"datasets must be of type {Benchmark.BenchmarkDataset}, {TimeSeries} or {(TimeSeries, TimeSeries)}, "
-                f"found type {type(dataset)}")
+                f"found type {type(dataset)}"
+            )
         multivariate = True
-        if (isinstance(dataset, Benchmark.BenchmarkDataset) and len(dataset.training_set.columns) < 2) \
-                or (isinstance(dataset, TimeSeries) and len(dataset.columns) < 2) \
-                or (isinstance(dataset, Tuple) and len(dataset[0].columns) < 2):
+        if (
+            (
+                isinstance(dataset, Benchmark.BenchmarkDataset)
+                and len(dataset.training_set.columns) < 2
+            )
+            or (isinstance(dataset, TimeSeries) and len(dataset.columns) < 2)
+            or (isinstance(dataset, Tuple) and len(dataset[0].columns) < 2)
+        ):
             multivariate = False
 
         if name is None:
             name = str(len(self.datasets))
         if isinstance(dataset, TimeSeries):
-            self.datasets.append(Benchmark.BenchmarkDataset(dataset, multivariate, name, target_columns=target_columns))
+            self.datasets.append(
+                Benchmark.BenchmarkDataset(
+                    dataset, multivariate, name, target_columns=target_columns
+                )
+            )
         elif isinstance(dataset, Tuple):
             self.datasets.append(
-                Benchmark.BenchmarkDataset(dataset[0], multivariate, name, dataset[1], target_columns=target_columns))
+                Benchmark.BenchmarkDataset(
+                    dataset[0],
+                    multivariate,
+                    name,
+                    dataset[1],
+                    target_columns=target_columns,
+                )
+            )
         else:  # it's a BenchmarkDataset
             self.datasets.append(dataset)
 
@@ -147,7 +195,9 @@ def get_metrics(self):
 
     def run(self, verbose: bool = False, debug: bool = False):
         # TODO: throw error if models or datasets is empty
-        self.results = {}  # self.results[model name][dataset name] = {metric name: value, metric name: value,...}
+        self.results = (
+            {}
+        )  # self.results[model name][dataset name] = {metric name: value, metric name: value,...}
         if verbose:
             print("Starting evaluation...")
         i = 0
@@ -169,7 +219,9 @@ def run(self, verbose: bool = False, debug: bool = False):
                         print(f"on dataset {dataset.name}, column {target} ")
 
                     # initialize variables
-                    train_set, test_set = dataset.get_train_test_split(self.train_proportion)
+                    train_set, test_set = dataset.get_train_test_split(
+                        self.train_proportion
+                    )
                     nb_features = len(train_set.columns)
                     train_size = len(train_set.time_index)
                     test_size = len(test_set.time_index)
@@ -183,15 +235,24 @@ def run(self, verbose: bool = False, debug: bool = False):
                     try:
                         # train
                         start_time = time.time()
-                        source_model.fit(train_set, test_set, target_column=target, multivariate=dataset.multivariate)
+                        source_model.fit(
+                            train_set,
+                            test_set,
+                            target_column=target,
+                            multivariate=dataset.multivariate,
+                        )
                         train_time = time.time() - start_time
 
                         # test
                         if verbose:
                             print(f"done, took {train_time}\ntesting... ", end="")
                         start_time = time.time()
-                        pred = source_model.predict(test_size, test_set, target_column=target,
-                                                    multivariate=dataset.multivariate)
+                        pred = source_model.predict(
+                            test_size,
+                            test_set,
+                            target_column=target,
+                            multivariate=dataset.multivariate,
+                        )
                         inference_time = time.time() - start_time
                         if verbose:
                             print(f"done, took {inference_time}")
@@ -209,26 +270,34 @@ def run(self, verbose: bool = False, debug: bool = False):
 
                     if train_success:
                         # compute metrics
-                        self.results[source_model.name][self._c(dataset.name, target)] = {
-                            'nb features': nb_features,
-                            'target column': target,
-                            'training set size': train_size,
-                            'training time': train_time,
-                            'test set size': test_size,
-                            'testing time': inference_time,
-                            'prediction': pred
+                        self.results[source_model.name][
+                            self._c(dataset.name, target)
+                        ] = {
+                            "nb features": nb_features,
+                            "target column": target,
+                            "training set size": train_size,
+                            "training time": train_time,
+                            "test set size": test_size,
+                            "testing time": inference_time,
+                            "prediction": pred,
                         }
                         # compute user-submitted metrics
                         for metric in self.metrics:
                             try:
                                 if verbose:
                                     print(f"{metric.name}: ", end="")
-                                    print(f'{test_set.columns} {target}')
-                                self.results[source_model.name] \
-                                    [self._c(dataset.name, target)] \
-                                    [metric.name] = metric.compute(test_set[target][:len(pred)], pred)
+                                    print(f"{test_set.columns} {target}")
+                                self.results[source_model.name][
+                                    self._c(dataset.name, target)
+                                ][metric.name] = metric.compute(
+                                    test_set[target][: len(pred)], pred
+                                )
                                 if verbose:
-                                    print(self.results[source_model.name][self._c(dataset.name, target)][metric.name])
+                                    print(
+                                        self.results[source_model.name][
+                                            self._c(dataset.name, target)
+                                        ][metric.name]
+                                    )
                             except:  # can't compute current metric
                                 print(f"Couldn't compute {metric.name}")
                                 if debug:
@@ -237,21 +306,25 @@ def run(self, verbose: bool = False, debug: bool = False):
             # back to model-level stats
             supports_uni = None
             if len(self.datasets) - mv0 > 0:  # if we have univariate ds in our batch
-                supports_uni = (nb_uv_run_failed > 0)
+                supports_uni = nb_uv_run_failed > 0
             supports_mult = None
             if mv0 > 0:
-                supports_mult = (nb_mv_run_failed > 0)
-            self.results[source_model.name]['supports univariate'] = Benchmark._bool_to_symbol(supports_uni)
-            self.results[source_model.name]['supports multivariate'] = Benchmark._bool_to_symbol(supports_mult)
+                supports_mult = nb_mv_run_failed > 0
+            self.results[source_model.name][
+                "supports univariate"
+            ] = Benchmark._bool_to_symbol(supports_uni)
+            self.results[source_model.name][
+                "supports multivariate"
+            ] = Benchmark._bool_to_symbol(supports_mult)
             i += 1
 
     @staticmethod
     def _bool_to_symbol(b: bool) -> str:
         if b is None:
-            return 'unknown'
+            return "unknown"
         if b:
-            return '✓'
-        return 'X'
+            return "✓"
+        return "X"
 
     def get_report(self):
         if self.results is None:
@@ -265,11 +338,16 @@ def get_report(self):
             for dataset in self.datasets:
                 for target in dataset.target_columns:
                     s += f"Dataset {dataset.name}, {target}:\n"
-                    if self._c(dataset.name, target) not in self.results[model.name].keys():
+                    if (
+                        self._c(dataset.name, target)
+                        not in self.results[model.name].keys()
+                    ):
                         s += f"{dataset.name}, {target}: couldn't complete training\n"
                     else:
-                        for key in self.results[model.name][self._c(dataset.name, target)].keys():
-                            if key == 'prediction':
+                        for key in self.results[model.name][
+                            self._c(dataset.name, target)
+                        ].keys():
+                            if key == "prediction":
                                 continue
                             s += f"{key}: {self.results[model.name][self._c(dataset.name, target)][key]}\n"
             txt.append(s)
@@ -282,14 +360,18 @@ def get_report_dataframes(self) -> dict:
         # index = datasets
         # columns = metric and details
         col0 = self.datasets[0].training_set.columns.tolist()[0]
-        me_columns = self.results[self.models[0].name][self._c(self.datasets[0].name, col0)].keys()
+        me_columns = self.results[self.models[0].name][
+            self._c(self.datasets[0].name, col0)
+        ].keys()
         reports = {}
         for model in self.models:
             report_model = pd.DataFrame(columns=me_columns)
             for dataset in self.datasets:
                 for target in dataset.target_columns:
                     if self._c(dataset.name, target) in self.results[model.name].keys():
-                        report_dict = self.results[model.name][self._c(dataset.name, target)]
+                        report_dict = self.results[model.name][
+                            self._c(dataset.name, target)
+                        ]
                         del report_dict["prediction"]
                         report_model.loc[dataset.name] = report_dict
 
@@ -300,4 +382,4 @@ def get_results(self):
         return self.results
 
     def _c(self, a, b):
-        return f'{a} {b}'
+        return f"{a} {b}"
diff --git a/src/ontime/module/processing/common.py b/src/ontime/module/processing/common.py
index 6b7b92d..c2ad780 100644
--- a/src/ontime/module/processing/common.py
+++ b/src/ontime/module/processing/common.py
@@ -90,10 +90,10 @@ def split_in_windows(ts: TimeSeries, window_length: int, stride_length: int) ->
 
 
 def split_inputs_from_targets(
-        ts_list: list,
-        input_length: int,
-        target_length: int,
-        gap_length: int = 0,
+    ts_list: list,
+    input_length: int,
+    target_length: int,
+    gap_length: int = 0,
 ) -> tuple:
     """
     Split a list of TimeSeries into input and target TimeSeries
@@ -114,8 +114,9 @@ def split_inputs_from_targets(
     # Iterate over each DataFrame in the list
     for df in dfs:
         # Check
-        assert input_length + target_length + gap_length <= len(df), \
-            "input_length + target_length + gap_length is longer that the total length of the DataFrame."
+        assert input_length + target_length + gap_length <= len(
+            df
+        ), "input_length + target_length + gap_length is longer that the total length of the DataFrame."
 
         # Create inputs
         input_series = df.iloc[:input_length]
diff --git a/src/ontime/module/processing/pytorch/sliced_dataset.py b/src/ontime/module/processing/pytorch/sliced_dataset.py
index 7a9a448..b31639a 100644
--- a/src/ontime/module/processing/pytorch/sliced_dataset.py
+++ b/src/ontime/module/processing/pytorch/sliced_dataset.py
@@ -6,10 +6,7 @@
 
 
 class SlicedDataset(Dataset):
-    def __init__(self,
-                 data: TimeSeries,
-                 period: int,
-                 labels: TimeSeries = None):
+    def __init__(self, data: TimeSeries, period: int, labels: TimeSeries = None):
         """
         :param data: TimeSeries of the data to model
         :param period: length of the input window
@@ -20,7 +17,7 @@ def __init__(self,
     def __len__(self):
         return len(self.data)
 
-    def __getitem__(self, idx: int) -> Tuple[np.ndarray, np.ndarray] :
+    def __getitem__(self, idx: int) -> Tuple[np.ndarray, np.ndarray]:
         input = self.data[idx]
         if self.labels is not None:
             output = self.labels[idx]
@@ -28,7 +25,9 @@ def __getitem__(self, idx: int) -> Tuple[np.ndarray, np.ndarray] :
             output = self.data[idx]
         return input, output
 
-    def slice(self, data: TimeSeries, period: int, labels: TimeSeries = None) -> Tuple[list, list]:
+    def slice(
+        self, data: TimeSeries, period: int, labels: TimeSeries = None
+    ) -> Tuple[list, list]:
         sliced_data = pp.common.split_in_windows(data, period, period)
         sliced_data = [elt.pd_dataframe().to_numpy() for elt in sliced_data]
         sliced_labels = None
diff --git a/src/ontime/module/processing/pytorch/time_series_data_module.py b/src/ontime/module/processing/pytorch/time_series_data_module.py
index cfdce44..5a74e46 100644
--- a/src/ontime/module/processing/pytorch/time_series_data_module.py
+++ b/src/ontime/module/processing/pytorch/time_series_data_module.py
@@ -10,20 +10,19 @@
 
 
 class TimeSeriesDataModule(L.LightningDataModule):
-
-    def __init__(self,
-                 series,
-                 window_length: int,
-                 stride_length: int,
-                 input_length: int,
-                 target_length: int,
-                 gap_length: int,
-                 batch_size: int = 32,
-                 num_workers: int = 0,
-                 train_split: Union[float, int or pd.TimeStamp] = None,
-                 transform: Pipeline = None,
-                 ):
-
+    def __init__(
+        self,
+        series,
+        window_length: int,
+        stride_length: int,
+        input_length: int,
+        target_length: int,
+        gap_length: int,
+        batch_size: int = 32,
+        num_workers: int = 0,
+        train_split: Union[float, int or pd.TimeStamp] = None,
+        transform: Pipeline = None,
+    ):
         super().__init__()
         # Main dataset
         self.series = series
@@ -63,10 +62,11 @@ def compute_split(self):
         """
         Splits the series from the datamodule given the parameters defined in the instance
         """
-        tmp_ts_train, self.ts_test = train_test_split(self.series, train_split=self.train_split)
+        tmp_ts_train, self.ts_test = train_test_split(
+            self.series, train_split=self.train_split
+        )
         self.ts_train, self.ts_val = train_test_split(tmp_ts_train, train_split=0.8)
 
-
     def compute_transform(self):
         """
         Compute data transformations
@@ -77,7 +77,6 @@ def compute_transform(self):
         self.ts_test_enc = self.transform.transform(self.ts_test)
 
     def setup(self, stage: str):
-
         self.compute_split()
         if self.transform is not None:
             self.compute_transform()
@@ -89,7 +88,7 @@ def setup(self, stage: str):
                 self.stride_length,
                 self.input_length,
                 self.target_length,
-                self.gap_length
+                self.gap_length,
             )
         elif stage == "validate":
             self.val = create_dataset(
@@ -98,7 +97,7 @@ def setup(self, stage: str):
                 self.stride_length,
                 self.input_length,
                 self.target_length,
-                self.gap_length
+                self.gap_length,
             )
         elif stage == "test":
             self.test = create_dataset(
@@ -107,25 +106,25 @@ def setup(self, stage: str):
                 self.stride_length,
                 self.input_length,
                 self.target_length,
-                self.gap_length
+                self.gap_length,
             )
         elif stage == "predict":
             raise NotImplementedError
 
     def train_dataloader(self):
-        return DataLoader(self.train,
-                          batch_size=self.batch_size,
-                          num_workers=self.num_workers)
+        return DataLoader(
+            self.train, batch_size=self.batch_size, num_workers=self.num_workers
+        )
 
     def val_dataloader(self):
-        return DataLoader(self.val,
-                          batch_size=self.batch_size,
-                          num_workers=self.num_workers)
+        return DataLoader(
+            self.val, batch_size=self.batch_size, num_workers=self.num_workers
+        )
 
     def test_dataloader(self):
-        return DataLoader(self.test,
-                          batch_size=self.batch_size,
-                          num_workers=self.num_workers)
+        return DataLoader(
+            self.test, batch_size=self.batch_size, num_workers=self.num_workers
+        )
 
     def predict_dataloader(self):
         raise NotImplementedError
diff --git a/src/ontime/module/processing/pytorch/time_series_dataset.py b/src/ontime/module/processing/pytorch/time_series_dataset.py
index 3a49770..cf76af9 100644
--- a/src/ontime/module/processing/pytorch/time_series_dataset.py
+++ b/src/ontime/module/processing/pytorch/time_series_dataset.py
@@ -25,4 +25,3 @@ def __getitem__(self, index):
         label_tensor = torch.tensor(self.labels_array[index], dtype=torch.float32)
 
         return data_tensor, label_tensor
-
diff --git a/src/ontime/module/processing/pytorch/utils.py b/src/ontime/module/processing/pytorch/utils.py
index 678f0b7..343679c 100644
--- a/src/ontime/module/processing/pytorch/utils.py
+++ b/src/ontime/module/processing/pytorch/utils.py
@@ -1,18 +1,28 @@
 import numpy as np
-from ontime.module.processing.common import split_in_windows, split_inputs_from_targets, timeseries_list_to_numpy
+from ontime.module.processing.common import (
+    split_in_windows,
+    split_inputs_from_targets,
+    timeseries_list_to_numpy,
+)
 from ontime.core.time_series import TimeSeries
 from .time_series_dataset import TimeSeriesDataset
 
 
-def create_dataset(ts: TimeSeries,
-                   window_length: int,
-                   stride_length: int,
-                   input_length: int,
-                   target_length: int,
-                   gap_length: int = 0):
+def create_dataset(
+    ts: TimeSeries,
+    window_length: int,
+    stride_length: int,
+    input_length: int,
+    target_length: int,
+    gap_length: int = 0,
+):
     ts_list = split_in_windows(ts, window_length, stride_length)
     input_ts_list, target_ts_list = split_inputs_from_targets(
-        ts_list, input_length=input_length, target_length=target_length, gap_length=gap_length)
+        ts_list,
+        input_length=input_length,
+        target_length=target_length,
+        gap_length=gap_length,
+    )
     features = timeseries_list_to_numpy(input_ts_list)
     labels = timeseries_list_to_numpy(target_ts_list)
     ds = TimeSeriesDataset(features, labels)
@@ -27,4 +37,4 @@ def dataset_to_numpy(dataset: TimeSeriesDataset):
         labels_list.append(labels)
     data_array = np.array(data_list)
     labels_array = np.array(labels_list)
-    return data_array, labels_array
\ No newline at end of file
+    return data_array, labels_array
diff --git a/src/ontime/module/processing/tensorflow/utils.py b/src/ontime/module/processing/tensorflow/utils.py
index 67a71d5..ba1c5cd 100644
--- a/src/ontime/module/processing/tensorflow/utils.py
+++ b/src/ontime/module/processing/tensorflow/utils.py
@@ -1,15 +1,21 @@
 import numpy as np
 from tensorflow.data import Dataset
-from ontime.module.processing.common import split_in_windows, split_inputs_from_targets, timeseries_list_to_numpy
+from ontime.module.processing.common import (
+    split_in_windows,
+    split_inputs_from_targets,
+    timeseries_list_to_numpy,
+)
 from ontime.core.time_series import TimeSeries
 
 
-def create_dataset(ts: TimeSeries,
-                   window_length: int,
-                   stride_length: int,
-                   input_length: int,
-                   target_length: int,
-                   gap_length: int = 0):
+def create_dataset(
+    ts: TimeSeries,
+    window_length: int,
+    stride_length: int,
+    input_length: int,
+    target_length: int,
+    gap_length: int = 0,
+):
     """
     Create a Tensorflow Dataset given a TimeSeries
 
@@ -24,7 +30,11 @@ def create_dataset(ts: TimeSeries,
 
     ts_list = split_in_windows(ts, window_length, stride_length)
     input_ts_list, target_ts_list = split_inputs_from_targets(
-        ts_list, input_length=input_length, target_length=target_length, gap_length=gap_length)
+        ts_list,
+        input_length=input_length,
+        target_length=target_length,
+        gap_length=gap_length,
+    )
     features = timeseries_list_to_numpy(input_ts_list)
     labels = timeseries_list_to_numpy(target_ts_list)
     ds_feature = Dataset.from_tensor_slices(features)
@@ -54,7 +64,7 @@ def dataset_to_numpy(dataset: Dataset):
 def arr_to_ts(arr: np.array) -> TimeSeries:
     """
     Converts on array obtained as output of a model to a TimeSeries
-    
+
     :param arr: Numpy array
     :return: TimeSeries
     """
@@ -65,7 +75,7 @@ def arr_to_ts(arr: np.array) -> TimeSeries:
 def get_input(dataset: Dataset) -> np.array:
     """
     Get the input from a Tensorflow Dataset
-    
+
     :param dataset: Tensorflow Dataset
     """
     return np.array([x for x, y in dataset])