ML_RUL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Iterative prediction of battery capacity using traditional ML algorithms:\n",
    "\n",
    "Use different models for time series prediction of fixed length input and fixed length output. Iteratively predict the capacity decline to the EOL of the battery, and obtain the predicted RUL.\n",
    "\n",
    "\n",
    "From [scikit-learn](https://scikit-learn.org/stable/): Linear Regression, KNeighborsRegressor, SVR, GaussianProcessRegressor, RandomForestRegressor, MLPRegressor\n",
    "From sktime: AutoARIMA\n",
    "From XGBoost: XGBRegressor\n",
    "From lightGBM: LGBMRegressor\n",
    "From CatBoost: CatBoostRegressor\n",
    "From Prophet: Prophet\n",
    "\n",
    "Models that can predict confidence intervals for results: GPR(Based on Stochastic process)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Conent\n",
    "1. Define hyper-parameters and process data. (required for all models)\n",
    "2. Provide an example of a dynamic graph using LR for iterative prediction.\n",
    "3. Use the scikit-learn model for prediction.  ['LR', 'KNN', 'GPR', 'MLP', 'RF']\n",
    "4. Use GPR for prediction with confidence intervals.\n",
    "5. Use AutoARIMA for prediction with confidence intervals.\n",
    "6. Use XGBoost, lightGBM, CatBoost for prediction.\n",
    "7. Use Prophet for prediction.\n",
    "\n",
    "**Note:**\n",
    "1. Gaussian Process Regression (GPR) may not be suitable for auto-regressive time series prediction,\n",
    "    because it need calculates the covariance matrix based on the distance between the input data points. (Discuss)\n",
    "    <font color=\"orange\">[ if you need use GPR for time series prediction, you can use cycle_num or time as exogeneous data. ]</font>\n",
    "\n",
    "\n",
    "\n",
    "**TODO<font color=\"orange\">[for benchmark v2.0]</font>:**\n",
    "Many models for multivariate time series forecasting employ a column-wise approach into multiple individual univariate time series,\n",
    "predicting them separately, and then merging the forecasted results back into a multivariate time series.\n",
    "\n",
    "However, some models are true multivariate time series forecasting models, such as the VAR model.\n",
    "When predicting the time series data for a variable, these models simultaneously take into account the values of other variables.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define random seeds and process data. (required for all models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2023-08-08T09:19:25.451296300Z",
     "start_time": "2023-08-08T09:19:25.321027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Define random seeds and process data. \"\"\"\n",
    "import copy\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "\n",
    "\"\"\" Define random seeds. \"\"\"\n",
    "random_seed = 42\n",
    "random.seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "sklearn.utils.check_random_state(random_seed)\n",
    "\n",
    "\"\"\" Define data source. \"\"\"\n",
    "data_path_dic = {\n",
    "    'NASA': 'data/NASA/',\n",
    "    'CALCE': 'data/CALCE/',\n",
    "}\n",
    "\n",
    "data_group_list_dic = {\n",
    "    'NASA': ['data'],   # ['B0005', 'B0006', 'B0007', 'B0018']\n",
    "    'CALCE': ['data'],\n",
    "}\n",
    "\n",
    "data_EOL_dic = {\n",
    "    'NASA': 1.4,\n",
    "    'CALCE': 1.4,\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "class DataProcess(object):\n",
    "    \"\"\"\n",
    "    Processing data for fixed-length input and output predictions.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    val_flag : bool, default=False\n",
    "        When using libraries such as scikit-learn, their models are trained using\n",
    "        cross-validation without the need for additional validation sets.\n",
    "\n",
    "    individual : bool, default=False\n",
    "        If false, then use the front part of each battery's complete lifecycle data as the training set,\n",
    "        and the data from the later part of the lifecycle as the test and validation sets.\n",
    "\n",
    "        If true, then use complete data from some batteries as the training set,\n",
    "        while the complete data from other batteries will be used as separate test and validation sets.\n",
    "        \n",
    "    \"\"\"\n",
    "    def __init__(self, val_flag=False, individual=True):\n",
    "        self.val_flag = val_flag   # Whether to use independent validation set.\n",
    "        self.individual = individual    # Choose different training set and test set partitioning modes.\n",
    "\n",
    "\n",
    "    def train_test_split(self, data:pd.DataFrame, seq_len, pred_len, test_ratio=0.2):\n",
    "        # TODO: 绘图展示训练集和测试集。individual等于true时将训练集和测试集分别绘制到两张图，否则绘制到一张图中，使用两种颜色。\n",
    "        data = copy.deepcopy(data)\n",
    "        if not self.individual:\n",
    "            # Convert type of data to list[list[float]] from pd.DataFrame.\n",
    "            data_temp = []\n",
    "            for i in range(data.shape[1]):\n",
    "                data_temp.append(data.iloc[:, i].dropna().values.tolist())\n",
    "            data = data_temp\n",
    "            fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "            if not self.val_flag:\n",
    "                train_x, train_y = [], []\n",
    "                test_x, test_y, test_RUL = [], [], []\n",
    "\n",
    "                train_data, test_data = data[:-round(len(data)*test_ratio)], data[-round(len(data)*test_ratio):]\n",
    "\n",
    "                for i in range(len(train_data)):\n",
    "                    axs[0].plot(train_data[i], color='blue')\n",
    "                    for j in range(len(train_data[i])-seq_len-pred_len+1):\n",
    "                        train_x.append(train_data[i][j:j+seq_len])\n",
    "                        train_y.append(train_data[i][j+seq_len:j+seq_len+pred_len])\n",
    "                for i in range(len(test_data)):\n",
    "                    lifespan = len(test_data[i])\n",
    "                    axs[1].plot(test_data[i], color='orange')\n",
    "                    for j in range(len(test_data[i])-seq_len-pred_len+1):\n",
    "                        test_x.append(test_data[i][j:j+seq_len])\n",
    "                        test_y.append(test_data[i][j+seq_len:j+seq_len+pred_len])\n",
    "                        test_RUL.append(lifespan - j - seq_len)\n",
    "                axs[0].set_title('Train data')\n",
    "                axs[1].set_title('Test data')\n",
    "                plt.show()\n",
    "                return train_x, train_y, test_x, test_y, test_RUL\n",
    "\n",
    "            else:\n",
    "                train_x, train_y = [], []\n",
    "                val_x, val_y = [], []\n",
    "                test_x, test_y, test_RUL = [], [], []\n",
    "                ...\n",
    "                return train_x, train_y, val_x, val_y, test_x, test_y\n",
    "        else:\n",
    "            if not self.val_flag:\n",
    "                pass\n",
    "            else:\n",
    "                pass\n",
    "\n",
    "\n",
    "    def process_data_2_RUL(self, data_name:str, data_group:str, EOL, seq_len, pred_len, val_flag=None, individual=None, test_ratio=0.2):\n",
    "        self.val_flag = val_flag if val_flag is not None else self.val_flag\n",
    "        self.individual = individual if individual is not None else self.individual\n",
    "        data_load_method = getattr(self, 'load_' + data_name + '_data')\n",
    "        data = data_load_method(data_path_dic[data_name], data_group)\n",
    "\n",
    "        return data, self.train_test_split(data, seq_len, pred_len, test_ratio)\n",
    "\n",
    "\n",
    "    def load_NASA_data(self, data_path, data_group):\n",
    "        data = pd.read_csv(data_path + data_group + '.csv')\n",
    "        return data\n",
    "\n",
    "    def load_CALCE_data(self, data_path, data_group):\n",
    "        pass\n",
    "\n",
    "\n",
    "\"\"\" Define hyper-parameters. \"\"\"\n",
    "seq_len, pred_len = 10, 8\n",
    "test_ratio = 0.2\n",
    "\n",
    "data_loader = DataProcess()\n",
    "data_source = 'NASA'\n",
    "EOL = data_EOL_dic[data_source]\n",
    "data_group = data_group_list_dic[data_source][0]\n",
    "raw_data, (train_x, train_y, test_x, test_y, test_RUL) = \\\n",
    "                                            data_loader.process_data_2_RUL(\n",
    "                                                data_source, data_group, EOL,\n",
    "                                                seq_len=seq_len, pred_len=pred_len, val_flag=False, individual=False,\n",
    "                                                test_ratio=test_ratio\n",
    "                                            )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Visualize the dynamic process of using LR iteration to predict capacity.\n",
    "using NASA data as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-07T01:51:10.902341100Z",
     "start_time": "2023-08-07T01:51:09.709588300Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2100x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Define a dynamic graph using LR for iterative prediction. \"\"\"\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "real_capacity = pd.read_csv('data/NASA/data.csv').iloc[:, -1].dropna().values.tolist()\n",
    "model = LinearRegression().fit(train_x, train_y)\n",
    "\n",
    "\n",
    "def iterative_pred(model, input, EOL):\n",
    "    # Iterative prediction\n",
    "    input_list, pred_y = [input], []\n",
    "    while True:\n",
    "        pred_y.append(model.predict([input])[0])\n",
    "        if all(pred_y[-1] > EOL):\n",
    "            input = np.concatenate([input[len(pred_y[-1]):], pred_y[-1]])\n",
    "            input_list.append(input)\n",
    "        else:\n",
    "            break\n",
    "    return input_list, pred_y\n",
    "\n",
    "class visual_iterative_pred():\n",
    "    def __init__(self, model, input, start, EOL, real_capacity, ax):\n",
    "        self.input_list, self.pred_y = iterative_pred(model, input, EOL)\n",
    "        self.in_len, self.out_len, self.s = len(input), len(self.pred_y[0]), start\n",
    "        capacity, = ax.plot(real_capacity, color='k', linewidth=2, markersize=3, marker='o', linestyle='dashed', label='Capacity')\n",
    "        raw_input, = ax.plot(np.arange(self.s, self.s+len(input)), input, color='b', linewidth=2, marker='*', markersize=6, label='raw_input')\n",
    "        EOL_im = ax.axhline(y=EOL, color='r', linestyle='dashed', linewidth=2, label='EOL')\n",
    "        self.input_im, = ax.plot([], [], color='r', linewidth=2, marker='o', markersize=6, label='iter_input')\n",
    "        self.predicted, = ax.plot([], [], color='green', linewidth=3, markersize=12, label='predicted')\n",
    "        self.pred_im, = ax.plot(np.arange(self.s+self.in_len, self.out_len + self.s+self.in_len), self.pred_y[0],\n",
    "                                color='lime', linewidth=2, marker='*', markersize=4, label='predicting')\n",
    "        ax.legend()\n",
    "        ax.set_xlabel('Cycle')\n",
    "        ax.set_ylabel('Capacity(Ahr)')\n",
    "        self.ims = [capacity, raw_input, EOL_im, self.pred_im, self.input_im]\n",
    "\n",
    "    def my_update(self, i):\n",
    "        self.input_im.set_data(np.arange(self.s+i*8, self.s+i*8+self.in_len), self.input_list[i])\n",
    "        self.pred_im.set_data(np.arange(self.s+i * self.out_len + self.in_len, self.s+(i + 1) * self.out_len + self.in_len),\n",
    "                              self.pred_y[i])\n",
    "        if i > 0:\n",
    "            self.predicted.set_data(np.arange(self.s+self.in_len, self.s+i*self.out_len+self.in_len), np.concatenate(self.pred_y[:i]))\n",
    "\n",
    "        return self.ims\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4), dpi=300)\n",
    "start_cycle = 25\n",
    "scope = visual_iterative_pred(model, test_x[start_cycle], start_cycle, 1.4, real_capacity, ax)\n",
    "\n",
    "# pass a generator in \"emitter\" to produce data for the update func\n",
    "ani = animation.FuncAnimation(fig, scope.my_update, range(len(scope.pred_y)), interval=250, blit=True)\n",
    "ani.save(f'example_LR_NASA_{start_cycle}.gif', writer='pillow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Use the scikit-learn library for prediction.\n",
    "model_list = ['LR', 'KNN', 'GPR', 'MLP', 'RF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "ExecuteTime": {
     "end_time": "2023-08-07T03:34:29.098864300Z",
     "start_time": "2023-08-07T03:34:27.625515900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR prediction loss:\n",
      "Capacity prediction loss:\n",
      "LR MSE: 0.0011964169953002234\n",
      "LR MAE: 0.024553007504984497\n",
      "RUL prediction loss:\n",
      "LR MSE: 4859.412280701755\n",
      "LR MAE: 61.79824561403509\n",
      "\n",
      "\n",
      "\n",
      "KNN prediction loss:\n",
      "Capacity prediction loss:\n",
      "KNN MSE: 0.001457568078157394\n",
      "KNN MAE: 0.02814916653575678\n",
      "RUL prediction loss:\n",
      "KNN MSE: 4779.236842105263\n",
      "KNN MAE: 61.18421052631579\n",
      "\n",
      "\n",
      "\n",
      "GPR prediction loss:\n",
      "Capacity prediction loss:\n",
      "GPR MSE: 0.02884631032436769\n",
      "GPR MAE: 0.08867755716704291\n",
      "RUL prediction loss:\n",
      "GPR MSE: 4847.561403508772\n",
      "GPR MAE: 61.578947368421055\n",
      "\n",
      "\n",
      "\n",
      "MLP prediction loss:\n",
      "Capacity prediction loss:\n",
      "MLP MSE: 0.0014956275221218271\n",
      "MLP MAE: 0.030441435531619883\n",
      "RUL prediction loss:\n",
      "MLP MSE: 4794.298245614035\n",
      "MLP MAE: 60.666666666666664\n",
      "\n",
      "\n",
      "\n",
      "RF prediction loss:\n",
      "Capacity prediction loss:\n",
      "RF MSE: 0.0011106284852250587\n",
      "RF MAE: 0.022990206295421218\n",
      "RUL prediction loss:\n",
      "RF MSE: 4955.482456140351\n",
      "RF MAE: 62.3421052631579\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "def build_model(model_name: str):\n",
    "    model_dict = {\n",
    "        'LR': 'from sklearn.linear_model import LinearRegression as Model',\n",
    "        'KNN': 'from sklearn.neighbors import KNeighborsRegressor as Model',\n",
    "        'GPR': 'from sklearn.gaussian_process import GaussianProcessRegressor as Model',\n",
    "        'MLP': 'from sklearn.neural_network import MLPRegressor as Model',\n",
    "        'RF': 'from sklearn.ensemble import RandomForestRegressor as Model',\n",
    "    }\n",
    "    exec(model_dict[model_name])\n",
    "    _model = eval(\"Model()\")\n",
    "    return _model\n",
    "\n",
    "\n",
    "def RUL_pred(model, input, EOL):\n",
    "    # Iterative prediction\n",
    "    pred_y = [model.predict([input])[0]]\n",
    "    pred_len = len(pred_y[0])\n",
    "    while True:\n",
    "        if all(pred_y[-1] > EOL):\n",
    "            input = np.concatenate([input[pred_len:], pred_y[-1]])\n",
    "        else:\n",
    "            break\n",
    "        pred_y.append(model.predict([input])[0])\n",
    "    return np.argmin(abs(pred_y[-1] - EOL))\n",
    "\n",
    "\n",
    "model_list = ['LR', 'KNN', 'GPR', 'MLP', 'RF']\n",
    "\n",
    "for model_name in model_list:\n",
    "    model = build_model(model_name).fit(train_x, train_y)\n",
    "    pred_y = model.predict(test_x)\n",
    "    print(f'{model_name} prediction loss:')\n",
    "    print('Capacity prediction loss:')\n",
    "    print(f'{model_name} MSE: {mean_squared_error(test_y, pred_y)}')\n",
    "    print(f'{model_name} MAE: {mean_absolute_error(test_y, pred_y)}')\n",
    "\n",
    "    pred_RUL = [RUL_pred(model, test_x[i], EOL) for i in range(len(test_x))]\n",
    "    print('RUL prediction loss:')\n",
    "    print(f'{model_name} MSE: {mean_squared_error(test_RUL, pred_RUL)}')\n",
    "    print(f'{model_name} MAE: {mean_absolute_error(test_RUL, pred_RUL)}')\n",
    "    print(2*'\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Use the sktime library for prediction.\n",
    "Use AutoARIMA, LightGBM, Prophet for prediction with confidence intervals."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from sklearn.neighbors import KNeighborsRegressor\n",
      "KNeighborsRegressor(**model_params)\n",
      "{}\n",
      "\n",
      "KNeighborsRegressor()\n",
      "from sktime.forecasting.arima import AutoARIMA\n",
      "AutoARIMA(**model_params)\n",
      "{}\n",
      "\n",
      "AutoARIMA()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\compose\\_reduce.py:1478: UserWarning: The `scitype` of the given `estimator` cannot be inferred. Assuming \"tabular-regressor\" = scikit-learn regressor interface. If this warning is followed by an unexpected exception, please consider report as a bug on the sktime issue tracker.\n",
      "  warn(\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "E:\\RUL_venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Invalid `fh`. The type of the passed `fh` values is not supported. Please use one of ('int', 'range', '1D np.ndarray of type int', '1D np.ndarray of type timedelta or dateoffset', 'list of type int', 'pd.RangeIndex', 'pd.PeriodIndex', 'pd.DatetimeIndex', 'pd.TimedeltaIndex'), but found type <class 'numpy.ndarray'>, values = [[ 8.82187235 -4.10457004  4.14365755  9.10836851 15.63095403 32.01559253\n  33.12003937 36.94912825 24.9183556  18.07738763 20.07292009  7.2493246 ]]",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[6], line 134\u001B[0m\n\u001B[0;32m    131\u001B[0m fh \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marange(\u001B[38;5;28mlen\u001B[39m(y_test)) \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m    133\u001B[0m forecaster\u001B[38;5;241m.\u001B[39mfit(y_train)\n\u001B[1;32m--> 134\u001B[0m y_pred \u001B[38;5;241m=\u001B[39m \u001B[43mforecaster\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    135\u001B[0m plot_series(y_train, y_test, y_pred, labels\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_train\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_test\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_pred\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[0;32m    136\u001B[0m mean_absolute_percentage_error(y_test, y_pred, symmetric\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:404\u001B[0m, in \u001B[0;36mBaseForecaster.predict\u001B[1;34m(self, fh, X)\u001B[0m\n\u001B[0;32m    402\u001B[0m \u001B[38;5;66;03m# we call the ordinary _predict if no looping/vectorization needed\u001B[39;00m\n\u001B[0;32m    403\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_vectorized:\n\u001B[1;32m--> 404\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mX_inner\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    405\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    406\u001B[0m     \u001B[38;5;66;03m# otherwise we call the vectorized version of predict\u001B[39;00m\n\u001B[0;32m    407\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_vectorize(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredict\u001B[39m\u001B[38;5;124m\"\u001B[39m, X\u001B[38;5;241m=\u001B[39mX_inner, fh\u001B[38;5;241m=\u001B[39mfh)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\compose\\_pipeline.py:1002\u001B[0m, in \u001B[0;36mTransformedTargetForecaster._predict\u001B[1;34m(self, fh, X)\u001B[0m\n\u001B[0;32m    987\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_predict\u001B[39m(\u001B[38;5;28mself\u001B[39m, fh\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, X\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m    988\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Forecast time series at future horizon.\u001B[39;00m\n\u001B[0;32m    989\u001B[0m \n\u001B[0;32m    990\u001B[0m \u001B[38;5;124;03m    Parameters\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1000\u001B[0m \u001B[38;5;124;03m        Point predictions\u001B[39;00m\n\u001B[0;32m   1001\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1002\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mforecaster_\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1003\u001B[0m     \u001B[38;5;66;03m# inverse transform y_pred\u001B[39;00m\n\u001B[0;32m   1004\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_inverse_transform(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtransformers_pre_, y_pred, X)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:404\u001B[0m, in \u001B[0;36mBaseForecaster.predict\u001B[1;34m(self, fh, X)\u001B[0m\n\u001B[0;32m    402\u001B[0m \u001B[38;5;66;03m# we call the ordinary _predict if no looping/vectorization needed\u001B[39;00m\n\u001B[0;32m    403\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_vectorized:\n\u001B[1;32m--> 404\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mX_inner\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    405\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    406\u001B[0m     \u001B[38;5;66;03m# otherwise we call the vectorized version of predict\u001B[39;00m\n\u001B[0;32m    407\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_vectorize(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpredict\u001B[39m\u001B[38;5;124m\"\u001B[39m, X\u001B[38;5;241m=\u001B[39mX_inner, fh\u001B[38;5;241m=\u001B[39mfh)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_sktime.py:30\u001B[0m, in \u001B[0;36m_BaseWindowForecaster._predict\u001B[1;34m(self, fh, X)\u001B[0m\n\u001B[0;32m     28\u001B[0m \u001B[38;5;66;03m# all values are out-of-sample\u001B[39;00m\n\u001B[0;32m     29\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m fh\u001B[38;5;241m.\u001B[39mis_all_out_of_sample(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcutoff):\n\u001B[1;32m---> 30\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict_fixed_cutoff\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m     31\u001B[0m \u001B[43m        \u001B[49m\u001B[43mfh\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_out_of_sample\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcutoff\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\n\u001B[0;32m     32\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     34\u001B[0m \u001B[38;5;66;03m# all values are in-sample\u001B[39;00m\n\u001B[0;32m     35\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m fh\u001B[38;5;241m.\u001B[39mis_all_in_sample(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcutoff):\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_sktime.py:74\u001B[0m, in \u001B[0;36m_BaseWindowForecaster._predict_fixed_cutoff\u001B[1;34m(self, fh, X, return_pred_int, alpha)\u001B[0m\n\u001B[0;32m     59\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Make single-step or multi-step fixed cutoff predictions.\u001B[39;00m\n\u001B[0;32m     60\u001B[0m \n\u001B[0;32m     61\u001B[0m \u001B[38;5;124;03mParameters\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     71\u001B[0m \u001B[38;5;124;03my_pred = pd.Series or pd.DataFrame\u001B[39;00m\n\u001B[0;32m     72\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m     73\u001B[0m \u001B[38;5;66;03m# assert all(fh > 0)\u001B[39;00m\n\u001B[1;32m---> 74\u001B[0m y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict_last_window\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m     75\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_pred_int\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mreturn_pred_int\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43malpha\u001B[49m\n\u001B[0;32m     76\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     77\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(y_pred, pd\u001B[38;5;241m.\u001B[39mSeries) \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(y_pred, pd\u001B[38;5;241m.\u001B[39mDataFrame):\n\u001B[0;32m     78\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m y_pred\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\compose\\_reduce.py:919\u001B[0m, in \u001B[0;36m_RecursiveReducer._predict_last_window\u001B[1;34m(self, fh, X, return_pred_int, alpha)\u001B[0m\n\u001B[0;32m    916\u001B[0m     X_pred \u001B[38;5;241m=\u001B[39m X_pred\u001B[38;5;241m.\u001B[39mreshape(\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m    918\u001B[0m \u001B[38;5;66;03m# Generate predictions.\u001B[39;00m\n\u001B[1;32m--> 919\u001B[0m y_pred[i] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mestimator_\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_pred\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    921\u001B[0m \u001B[38;5;66;03m# Update last window with previous prediction.\u001B[39;00m\n\u001B[0;32m    922\u001B[0m last[:, \u001B[38;5;241m0\u001B[39m, window_length \u001B[38;5;241m+\u001B[39m i] \u001B[38;5;241m=\u001B[39m y_pred[i]\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:397\u001B[0m, in \u001B[0;36mBaseForecaster.predict\u001B[1;34m(self, fh, X)\u001B[0m\n\u001B[0;32m    394\u001B[0m \u001B[38;5;66;03m# handle inputs\u001B[39;00m\n\u001B[0;32m    396\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcheck_is_fitted()\n\u001B[1;32m--> 397\u001B[0m fh \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_fh\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    399\u001B[0m \u001B[38;5;66;03m# input check and conversion for X\u001B[39;00m\n\u001B[0;32m    400\u001B[0m X_inner \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_X(X\u001B[38;5;241m=\u001B[39mX)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:1768\u001B[0m, in \u001B[0;36mBaseForecaster._check_fh\u001B[1;34m(self, fh)\u001B[0m\n\u001B[0;32m   1758\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1759\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe forecasting horizon `fh` must be passed to \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1760\u001B[0m             \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`fit` of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, but none was found. \u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m+\u001B[39m msg\n\u001B[0;32m   1761\u001B[0m         )\n\u001B[0;32m   1762\u001B[0m         \u001B[38;5;66;03m# in case C. fh is optional in fit:\u001B[39;00m\n\u001B[0;32m   1763\u001B[0m         \u001B[38;5;66;03m# this is fine, nothing to check/raise\u001B[39;00m\n\u001B[0;32m   1764\u001B[0m \n\u001B[0;32m   1765\u001B[0m \u001B[38;5;66;03m# B. fh is passed\u001B[39;00m\n\u001B[0;32m   1766\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1767\u001B[0m     \u001B[38;5;66;03m# If fh is passed, coerce to ForecastingHorizon and validate (all cases)\u001B[39;00m\n\u001B[1;32m-> 1768\u001B[0m     fh \u001B[38;5;241m=\u001B[39m \u001B[43mcheck_fh\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfreq\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_cutoff\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1770\u001B[0m     \u001B[38;5;66;03m# fh is written to self if one of the following is true\u001B[39;00m\n\u001B[0;32m   1771\u001B[0m     \u001B[38;5;66;03m# - estimator has not been fitted yet (for safety from side effects)\u001B[39;00m\n\u001B[0;32m   1772\u001B[0m     \u001B[38;5;66;03m# - fh has not been seen yet\u001B[39;00m\n\u001B[0;32m   1773\u001B[0m     \u001B[38;5;66;03m# - fh has been seen, but was optional in fit,\u001B[39;00m\n\u001B[0;32m   1774\u001B[0m     \u001B[38;5;66;03m#     this means fh needs not be same and can be overwritten\u001B[39;00m\n\u001B[0;32m   1775\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m requires_fh \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fh \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_fitted:\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\utils\\validation\\forecasting.py:291\u001B[0m, in \u001B[0;36mcheck_fh\u001B[1;34m(fh, enforce_relative, freq)\u001B[0m\n\u001B[0;32m    288\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msktime\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforecasting\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbase\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m ForecastingHorizon\n\u001B[0;32m    290\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(fh, ForecastingHorizon):\n\u001B[1;32m--> 291\u001B[0m     fh \u001B[38;5;241m=\u001B[39m \u001B[43mForecastingHorizon\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfh\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mis_relative\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfreq\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfreq\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    292\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    293\u001B[0m     fh\u001B[38;5;241m.\u001B[39mfreq \u001B[38;5;241m=\u001B[39m freq\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_fh.py:290\u001B[0m, in \u001B[0;36mForecastingHorizon.__init__\u001B[1;34m(self, values, is_relative, freq)\u001B[0m\n\u001B[0;32m    281\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\n\u001B[0;32m    282\u001B[0m     \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m    283\u001B[0m     values: Union[VALID_FORECASTING_HORIZON_TYPES] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    288\u001B[0m \n\u001B[0;32m    289\u001B[0m     \u001B[38;5;66;03m# values to pd.Index self._values\u001B[39;00m\n\u001B[1;32m--> 290\u001B[0m     values \u001B[38;5;241m=\u001B[39m \u001B[43m_check_values\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    291\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values \u001B[38;5;241m=\u001B[39m values\n\u001B[0;32m    293\u001B[0m     \u001B[38;5;66;03m# infer freq from values, if available\u001B[39;00m\n\u001B[0;32m    294\u001B[0m     \u001B[38;5;66;03m# if not, infer from freq argument, if available\u001B[39;00m\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_fh.py:134\u001B[0m, in \u001B[0;36m_check_values\u001B[1;34m(values)\u001B[0m\n\u001B[0;32m    124\u001B[0m \u001B[38;5;66;03m# otherwise, raise type error\u001B[39;00m\n\u001B[0;32m    125\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    126\u001B[0m     valid_types \u001B[38;5;241m=\u001B[39m (\n\u001B[0;32m    127\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mint\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    128\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrange\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    132\u001B[0m         \u001B[38;5;241m*\u001B[39m[\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpd.\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mindex_type\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index_type \u001B[38;5;129;01min\u001B[39;00m VALID_INDEX_TYPES],\n\u001B[0;32m    133\u001B[0m     )\n\u001B[1;32m--> 134\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[0;32m    135\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvalid `fh`. The type of the passed `fh` values is not supported. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    136\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPlease use one of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mvalid_types\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, but found type \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(values)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    137\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvalues = \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mvalues\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    138\u001B[0m     )\n\u001B[0;32m    140\u001B[0m \u001B[38;5;66;03m# check values does not contain duplicates\u001B[39;00m\n\u001B[0;32m    141\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(values) \u001B[38;5;241m!=\u001B[39m values\u001B[38;5;241m.\u001B[39mnunique():\n",
      "\u001B[1;31mTypeError\u001B[0m: Invalid `fh`. The type of the passed `fh` values is not supported. Please use one of ('int', 'range', '1D np.ndarray of type int', '1D np.ndarray of type timedelta or dateoffset', 'list of type int', 'pd.RangeIndex', 'pd.PeriodIndex', 'pd.DatetimeIndex', 'pd.TimedeltaIndex'), but found type <class 'numpy.ndarray'>, values = [[ 8.82187235 -4.10457004  4.14365755  9.10836851 15.63095403 32.01559253\n  33.12003937 36.94912825 24.9183556  18.07738763 20.07292009  7.2493246 ]]"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.compose import (\n",
    "    EnsembleForecaster,\n",
    "    MultiplexForecaster,\n",
    "    TransformedTargetForecaster,\n",
    "    make_reduction,\n",
    ")\n",
    "from sktime.forecasting.exp_smoothing import ExponentialSmoothing\n",
    "from sktime.forecasting.model_selection import (\n",
    "    ForecastingGridSearchCV,\n",
    "    SlidingWindowSplitter,\n",
    "    temporal_train_test_split,\n",
    ")\n",
    "from sktime.forecasting.naive import NaiveForecaster\n",
    "from sktime.forecasting.theta import ThetaForecaster\n",
    "from sktime.forecasting.bats import BATS\n",
    "from sktime.forecasting.tbats import TBATS\n",
    "\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.forecasting.trend import PolynomialTrendForecaster\n",
    "from sktime.forecasting.compose import make_reduction, TransformedTargetForecaster\n",
    "from sktime.transformations.series.detrend import Detrender, Deseasonalizer\n",
    "from sktime.forecasting.compose import EnsembleForecaster\n",
    "from sktime.forecasting.compose import MultiplexForecaster\n",
    "from sktime.forecasting.arima import AutoARIMA\n",
    "\n",
    "\n",
    "def get_transformed_target_forecaster(model_name, seq_len, **model_params):\n",
    "\n",
    "    #Initialize Light GBM Regressor\n",
    "    model_pack = {\n",
    "        'LightGBM': 'import lightgbm as lgb',\n",
    "        'AutoETS': 'from sktime.forecasting.ets import AutoETS',\n",
    "        'AutoARIMA': 'from sktime.forecasting.arima import AutoARIMA',\n",
    "        'Prophet': 'from sktime.forecasting.fbprophet import Prophet',\n",
    "        'KNN': 'from sklearn.neighbors import KNeighborsRegressor',\n",
    "\n",
    "    }\n",
    "    from sktime.forecasting.arima import AutoARIMA\n",
    "    print(model_pack[model_name])\n",
    "    exec(model_pack[model_name])    # import model package\n",
    "\n",
    "    model_dict = {\n",
    "        'LightGBM': \"lgb.LGBMRegressor(**model_params)\",\n",
    "        'AutoETS': \"AutoETS(**model_params)\",\n",
    "        'AutoARIMA': \"AutoARIMA(**model_params)\",\n",
    "        'Prophet': \"Prophet(**model_params)\",\n",
    "        'KNN': \"KNeighborsRegressor(**model_params)\",\n",
    "    }\n",
    "    print(model_dict[model_name])\n",
    "    print(model_params)\n",
    "    print(**model_params)\n",
    "    regressor = eval(model_dict[model_name])    # initialize model\n",
    "    print(regressor)\n",
    "\n",
    "    #1.Separate the Seasonal Component.\n",
    "    #2.Fit a forecaster for the trend.\n",
    "    #3.Fit a Autoregressor to the resdiual(autoregressing on four historic values).\n",
    "\n",
    "    forecaster = TransformedTargetForecaster(\n",
    "        [\n",
    "            (\"deseasonalise\", Deseasonalizer(model=\"multiplicative\", sp=12)),\n",
    "            (\"detrend\", Detrender(forecaster=PolynomialTrendForecaster(degree=1))),\n",
    "            (\n",
    "                # Recursive strategy for Multi-Step Ahead Forecast.\n",
    "                # Auto Regress on four previous values\n",
    "                \"forecast\",\n",
    "                make_reduction(\n",
    "                    AutoARIMA(), window_length=seq_len, strategy=\"recursive\",\n",
    "                ),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "    return forecaster\n",
    "\n",
    "# model_param = [\n",
    "#     ['LightGBM', 25]\n",
    "# ]\n",
    "\n",
    "model = get_transformed_target_forecaster('KNN', 12)\n",
    "# model = get_transformed_target_forecaster('LightGBM', 25, n_estimators=100, alpha=alpha, objective='quantile', reg_alpha=0.1)\n",
    "# model = get_transformed_target_forecaster('Prophet', 25)\n",
    "# model = get_transformed_target_forecaster('AutoETS', 25)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# for i in range(raw_data.shape[1]):\n",
    "#     data = raw_data.iloc[:, i].dropna()\n",
    "#     y_train, y_test = temporal_train_test_split(data, test_size=0.2)\n",
    "#     model.fit(y_train)\n",
    "#     y_pred = model.predict(fh)\n",
    "#     y_pred.index.name=\"date\"\n",
    "#     y_pred.name=f\"predicted_sales_q_{alpha}\"\n",
    "#     forecasts.append(y_pred)\n",
    "\n",
    "\n",
    "# imports necessary for this chapter\n",
    "from sktime.datasets import load_airline\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "from sktime.forecasting.compose import make_reduction\n",
    "\n",
    "\n",
    "# forecaster = TransformedTargetForecaster(\n",
    "#     [\n",
    "#         (\"deseasonalize\", Deseasonalizer(model=\"multiplicative\", sp=12)),\n",
    "#         (\"detrend\", Detrender(forecaster=PolynomialTrendForecaster(degree=1))),\n",
    "#         (\n",
    "#             \"forecast\",\n",
    "#             make_reduction(\n",
    "#                 KNeighborsRegressor(),\n",
    "#                 window_length=15,\n",
    "#                 strategy=\"recursive\",\n",
    "#             ),\n",
    "#         ),\n",
    "#     ]\n",
    "# )\n",
    "\n",
    "forecaster = get_transformed_target_forecaster('AutoARIMA', 12)\n",
    "\n",
    "# data loading for illustration (see section 1 for explanation)\n",
    "y = load_airline()\n",
    "y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
    "# fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
    "\n",
    "fh = np.arange(len(y_test)) + 1\n",
    "\n",
    "forecaster.fit(y_train)\n",
    "y_pred = forecaster.predict(fh)\n",
    "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"])\n",
    "mean_absolute_percentage_error(y_test, y_pred, symmetric=False)\n",
    "\n",
    "\n",
    "# test_num = round(raw_data.shape[1]*test_ratio)\n",
    "# train_num = raw_data.shape[1] - test_num\n",
    "# data = raw_data.iloc[:, 0].dropna()\n",
    "# model.fit(data)\n",
    "# for i in range(1,train_num):\n",
    "#     model.update(data.iloc[:, i].dropna())\n",
    "\n",
    "\n",
    "#Initialize ForecastingHorizon class to specify the horizon of forecast\n",
    "# fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
    "\n",
    "# y_pred = model.predict(fh)\n",
    "# y_pred.index.name=\"date\"\n",
    "# y_pred.name=f\"predicted_sales_q_{alpha}\"\n",
    "# forecasts.append(y_pred)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# quantiles = [.1, .5, .9]\n",
    "# forecasts = []\n",
    "#\n",
    "# #Iterate for each quantile.\n",
    "# for alpha in quantiles:\n",
    "#\n",
    "#     forecaster = get_transformed_target_forecaster(alpha,params)\n",
    "#\n",
    "#     #Initialize ForecastingHorizon class to specify the horizon of forecast\n",
    "#     fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
    "#\n",
    "#     #Fit on Training data.\n",
    "#     forecaster.fit(y_train)\n",
    "#\n",
    "#     #Forecast the values.\n",
    "#     y_pred = forecaster.predict(fh)\n",
    "#\n",
    "#     #List of forecasts made for each quantile.\n",
    "#     y_pred.index.name=\"date\"\n",
    "#     y_pred.name=f\"predicted_sales_q_{alpha}\"\n",
    "#     forecasts.append(y_pred)\n",
    "#\n",
    "# #Append the actual data for plotting.\n",
    "# store1_agg_monthly.index.name = \"date\"\n",
    "# store1_agg_monthly.name = \"original\"\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T07:53:28.494853600Z",
     "start_time": "2023-08-08T07:53:23.128766900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data: [0 1 2 3 4 5 6 7 8 9]\n",
      "Test data: [-0.99999021 -0.53657292  0.42016704  0.99060736  0.65028784]\n",
      "----------------------------------------\n",
      "Train data: [ 1  2  3  4  5  6  7  8  9 10]\n",
      "Test data: [-0.53657292  0.42016704  0.99060736  0.65028784 -0.28790332]\n",
      "----------------------------------------\n",
      "Train data: [ 2  3  4  5  6  7  8  9 10 11]\n",
      "Test data: [ 0.42016704  0.99060736  0.65028784 -0.28790332 -0.96139749]\n",
      "----------------------------------------\n",
      "Train data: [ 3  4  5  6  7  8  9 10 11 12]\n",
      "Test data: [ 0.99060736  0.65028784 -0.28790332 -0.96139749 -0.75098725]\n",
      "----------------------------------------\n",
      "Train data: [ 4  5  6  7  8  9 10 11 12 13]\n",
      "Test data: [ 0.65028784 -0.28790332 -0.96139749 -0.75098725  0.14987721]\n",
      "----------------------------------------\n",
      "Train data: [ 5  6  7  8  9 10 11 12 13 14]\n",
      "Test data: [-0.28790332 -0.96139749 -0.75098725  0.14987721  0.91294525]\n",
      "----------------------------------------\n",
      "Train data: [ 6  7  8  9 10 11 12 13 14 15]\n",
      "Test data: [-0.96139749 -0.75098725  0.14987721  0.91294525  0.83665564]\n",
      "----------------------------------------\n",
      "Train data: [ 7  8  9 10 11 12 13 14 15 16]\n",
      "Test data: [-0.75098725  0.14987721  0.91294525  0.83665564 -0.00885131]\n",
      "----------------------------------------\n",
      "Train data: [ 8  9 10 11 12 13 14 15 16 17]\n",
      "Test data: [ 0.14987721  0.91294525  0.83665564 -0.00885131 -0.8462204 ]\n",
      "----------------------------------------\n",
      "Train data: [ 9 10 11 12 13 14 15 16 17 18]\n",
      "Test data: [ 0.91294525  0.83665564 -0.00885131 -0.8462204  -0.90557836]\n",
      "----------------------------------------\n",
      "Train data: [10 11 12 13 14 15 16 17 18 19]\n",
      "Test data: [ 0.83665564 -0.00885131 -0.8462204  -0.90557836 -0.13235175]\n",
      "----------------------------------------\n",
      "Train data: [11 12 13 14 15 16 17 18 19 20]\n",
      "Test data: [-0.00885131 -0.8462204  -0.90557836 -0.13235175  0.76255845]\n",
      "----------------------------------------\n",
      "Train data: [12 13 14 15 16 17 18 19 20 21]\n",
      "Test data: [-0.8462204  -0.90557836 -0.13235175  0.76255845  0.95637593]\n",
      "----------------------------------------\n",
      "Train data: [13 14 15 16 17 18 19 20 21 22]\n",
      "Test data: [-0.90557836 -0.13235175  0.76255845  0.95637593  0.27090579]\n",
      "----------------------------------------\n",
      "Train data: [14 15 16 17 18 19 20 21 22 23]\n",
      "Test data: [-0.13235175  0.76255845  0.95637593  0.27090579 -0.66363388]\n",
      "----------------------------------------\n",
      "Train data: [15 16 17 18 19 20 21 22 23 24]\n",
      "Test data: [ 0.76255845  0.95637593  0.27090579 -0.66363388 -0.98803162]\n",
      "----------------------------------------\n",
      "Train data: [16 17 18 19 20 21 22 23 24 25]\n",
      "Test data: [ 0.95637593  0.27090579 -0.66363388 -0.98803162 -0.40403765]\n",
      "----------------------------------------\n",
      "Train data: [17 18 19 20 21 22 23 24 25 26]\n",
      "Test data: [ 0.27090579 -0.66363388 -0.98803162 -0.40403765  0.55142668]\n",
      "----------------------------------------\n",
      "Train data: [18 19 20 21 22 23 24 25 26 27]\n",
      "Test data: [-0.66363388 -0.98803162 -0.40403765  0.55142668  0.99991186]\n",
      "----------------------------------------\n",
      "Train data: [19 20 21 22 23 24 25 26 27 28]\n",
      "Test data: [-0.98803162 -0.40403765  0.55142668  0.99991186  0.52908269]\n",
      "----------------------------------------\n",
      "Train data: [20 21 22 23 24 25 26 27 28 29]\n",
      "Test data: [-0.40403765  0.55142668  0.99991186  0.52908269 -0.42818267]\n",
      "----------------------------------------\n",
      "Train data: [21 22 23 24 25 26 27 28 29 30]\n",
      "Test data: [ 0.55142668  0.99991186  0.52908269 -0.42818267 -0.99177885]\n",
      "----------------------------------------\n",
      "Train data: [22 23 24 25 26 27 28 29 30 31]\n",
      "Test data: [ 0.99991186  0.52908269 -0.42818267 -0.99177885 -0.64353813]\n",
      "----------------------------------------\n",
      "Train data: [23 24 25 26 27 28 29 30 31 32]\n",
      "Test data: [ 0.52908269 -0.42818267 -0.99177885 -0.64353813  0.29636858]\n",
      "----------------------------------------\n",
      "Train data: [24 25 26 27 28 29 30 31 32 33]\n",
      "Test data: [-0.42818267 -0.99177885 -0.64353813  0.29636858  0.96379539]\n",
      "----------------------------------------\n",
      "Train data: [25 26 27 28 29 30 31 32 33 34]\n",
      "Test data: [-0.99177885 -0.64353813  0.29636858  0.96379539  0.74511316]\n",
      "----------------------------------------\n",
      "Train data: [26 27 28 29 30 31 32 33 34 35]\n",
      "Test data: [-0.64353813  0.29636858  0.96379539  0.74511316 -0.15862267]\n",
      "----------------------------------------\n",
      "Train data: [27 28 29 30 31 32 33 34 35 36]\n",
      "Test data: [ 0.29636858  0.96379539  0.74511316 -0.15862267 -0.91652155]\n",
      "----------------------------------------\n",
      "Train data: [28 29 30 31 32 33 34 35 36 37]\n",
      "Test data: [ 0.96379539  0.74511316 -0.15862267 -0.91652155 -0.83177474]\n",
      "----------------------------------------\n",
      "Train data: [29 30 31 32 33 34 35 36 37 38]\n",
      "Test data: [ 0.74511316 -0.15862267 -0.91652155 -0.83177474  0.01770193]\n",
      "----------------------------------------\n",
      "Train data: [30 31 32 33 34 35 36 37 38 39]\n",
      "Test data: [-0.15862267 -0.91652155 -0.83177474  0.01770193  0.85090352]\n",
      "----------------------------------------\n",
      "Train data: [31 32 33 34 35 36 37 38 39 40]\n",
      "Test data: [-0.91652155 -0.83177474  0.01770193  0.85090352  0.90178835]\n",
      "----------------------------------------\n",
      "Train data: [32 33 34 35 36 37 38 39 40 41]\n",
      "Test data: [-0.83177474  0.01770193  0.85090352  0.90178835  0.12357312]\n",
      "----------------------------------------\n",
      "Train data: [33 34 35 36 37 38 39 40 41 42]\n",
      "Test data: [ 0.01770193  0.85090352  0.90178835  0.12357312 -0.76825466]\n",
      "----------------------------------------\n",
      "Train data: [34 35 36 37 38 39 40 41 42 43]\n",
      "Test data: [ 0.85090352  0.90178835  0.12357312 -0.76825466 -0.95375265]\n",
      "----------------------------------------\n",
      "Train data: [35 36 37 38 39 40 41 42 43 44]\n",
      "Test data: [ 0.90178835  0.12357312 -0.76825466 -0.95375265 -0.26237485]\n",
      "----------------------------------------\n",
      "Train data: [36 37 38 39 40 41 42 43 44 45]\n",
      "Test data: [ 0.12357312 -0.76825466 -0.95375265 -0.26237485  0.67022918]\n",
      "----------------------------------------\n",
      "Train data: [37 38 39 40 41 42 43 44 45 46]\n",
      "Test data: [-0.76825466 -0.95375265 -0.26237485  0.67022918  0.98662759]\n",
      "----------------------------------------\n",
      "Train data: [38 39 40 41 42 43 44 45 46 47]\n",
      "Test data: [-0.95375265 -0.26237485  0.67022918  0.98662759  0.39592515]\n",
      "----------------------------------------\n",
      "Train data: [39 40 41 42 43 44 45 46 47 48]\n",
      "Test data: [-0.26237485  0.67022918  0.98662759  0.39592515 -0.55878905]\n",
      "----------------------------------------\n",
      "Train data: [40 41 42 43 44 45 46 47 48 49]\n",
      "Test data: [ 0.67022918  0.98662759  0.39592515 -0.55878905 -0.99975517]\n",
      "----------------------------------------\n",
      "Train data: [41 42 43 44 45 46 47 48 49 50]\n",
      "Test data: [ 0.98662759  0.39592515 -0.55878905 -0.99975517 -0.521551  ]\n",
      "----------------------------------------\n",
      "Train data: [42 43 44 45 46 47 48 49 50 51]\n",
      "Test data: [ 0.39592515 -0.55878905 -0.99975517 -0.521551    0.43616476]\n",
      "----------------------------------------\n",
      "Train data: [43 44 45 46 47 48 49 50 51 52]\n",
      "Test data: [-0.55878905 -0.99975517 -0.521551    0.43616476  0.99287265]\n",
      "----------------------------------------\n",
      "Train data: [44 45 46 47 48 49 50 51 52 53]\n",
      "Test data: [-0.99975517 -0.521551    0.43616476  0.99287265  0.63673801]\n",
      "----------------------------------------\n",
      "Train data: [45 46 47 48 49 50 51 52 53 54]\n",
      "Test data: [-0.521551    0.43616476  0.99287265  0.63673801 -0.30481062]\n",
      "----------------------------------------\n",
      "Train data: [46 47 48 49 50 51 52 53 54 55]\n",
      "Test data: [ 0.43616476  0.99287265  0.63673801 -0.30481062 -0.96611777]\n",
      "----------------------------------------\n",
      "Train data: [47 48 49 50 51 52 53 54 55 56]\n",
      "Test data: [ 0.99287265  0.63673801 -0.30481062 -0.96611777 -0.7391807 ]\n",
      "----------------------------------------\n",
      "Train data: [48 49 50 51 52 53 54 55 56 57]\n",
      "Test data: [ 0.63673801 -0.30481062 -0.96611777 -0.7391807   0.1673557 ]\n",
      "----------------------------------------\n",
      "Train data: [49 50 51 52 53 54 55 56 57 58]\n",
      "Test data: [-0.30481062 -0.96611777 -0.7391807   0.1673557   0.92002604]\n",
      "----------------------------------------\n",
      "Train data: [50 51 52 53 54 55 56 57 58 59]\n",
      "Test data: [-0.96611777 -0.7391807   0.1673557   0.92002604  0.82682868]\n",
      "----------------------------------------\n",
      "Train data: [51 52 53 54 55 56 57 58 59 60]\n",
      "Test data: [-0.7391807   0.1673557   0.92002604  0.82682868 -0.02655115]\n",
      "----------------------------------------\n",
      "Train data: [52 53 54 55 56 57 58 59 60 61]\n",
      "Test data: [ 0.1673557   0.92002604  0.82682868 -0.02655115 -0.85551998]\n",
      "----------------------------------------\n",
      "Train data: [53 54 55 56 57 58 59 60 61 62]\n",
      "Test data: [ 0.92002604  0.82682868 -0.02655115 -0.85551998 -0.89792768]\n",
      "----------------------------------------\n",
      "Train data: [54 55 56 57 58 59 60 61 62 63]\n",
      "Test data: [ 0.82682868 -0.02655115 -0.85551998 -0.89792768 -0.11478481]\n",
      "----------------------------------------\n",
      "Train data: [55 56 57 58 59 60 61 62 63 64]\n",
      "Test data: [-0.02655115 -0.85551998 -0.89792768 -0.11478481  0.77389068]\n",
      "----------------------------------------\n",
      "Train data: [56 57 58 59 60 61 62 63 64 65]\n",
      "Test data: [-0.85551998 -0.89792768 -0.11478481  0.77389068  0.95105465]\n",
      "----------------------------------------\n",
      "Train data: [57 58 59 60 61 62 63 64 65 66]\n",
      "Test data: [-0.89792768 -0.11478481  0.77389068  0.95105465  0.25382336]\n",
      "----------------------------------------\n",
      "Train data: [58 59 60 61 62 63 64 65 66 67]\n",
      "Test data: [-0.11478481  0.77389068  0.95105465  0.25382336 -0.67677196]\n",
      "----------------------------------------\n",
      "Train data: [59 60 61 62 63 64 65 66 67 68]\n",
      "Test data: [ 0.77389068  0.95105465  0.25382336 -0.67677196 -0.98514626]\n",
      "----------------------------------------\n",
      "Train data: [60 61 62 63 64 65 66 67 68 69]\n",
      "Test data: [ 0.95105465  0.25382336 -0.67677196 -0.98514626 -0.38778164]\n",
      "----------------------------------------\n",
      "Train data: [61 62 63 64 65 66 67 68 69 70]\n",
      "Test data: [ 0.25382336 -0.67677196 -0.98514626 -0.38778164  0.56610764]\n",
      "----------------------------------------\n",
      "Train data: [62 63 64 65 66 67 68 69 70 71]\n",
      "Test data: [-0.67677196 -0.98514626 -0.38778164  0.56610764  0.99952016]\n",
      "----------------------------------------\n",
      "Train data: [63 64 65 66 67 68 69 70 71 72]\n",
      "Test data: [-0.98514626 -0.38778164  0.56610764  0.99952016  0.51397846]\n",
      "----------------------------------------\n",
      "Train data: [64 65 66 67 68 69 70 71 72 73]\n",
      "Test data: [-0.38778164  0.56610764  0.99952016  0.51397846 -0.44411267]\n",
      "----------------------------------------\n",
      "Train data: [65 66 67 68 69 70 71 72 73 74]\n",
      "Test data: [ 0.56610764  0.99952016  0.51397846 -0.44411267 -0.99388865]\n",
      "----------------------------------------\n",
      "Train data: [66 67 68 69 70 71 72 73 74 75]\n",
      "Test data: [ 0.99952016  0.51397846 -0.44411267 -0.99388865 -0.62988799]\n",
      "----------------------------------------\n",
      "Train data: [67 68 69 70 71 72 73 74 75 76]\n",
      "Test data: [ 0.51397846 -0.44411267 -0.99388865 -0.62988799  0.31322878]\n",
      "----------------------------------------\n",
      "Train data: [68 69 70 71 72 73 74 75 76 77]\n",
      "Test data: [-0.44411267 -0.99388865 -0.62988799  0.31322878  0.96836446]\n",
      "----------------------------------------\n",
      "Train data: [69 70 71 72 73 74 75 76 77 78]\n",
      "Test data: [-0.99388865 -0.62988799  0.31322878  0.96836446  0.73319032]\n",
      "----------------------------------------\n",
      "Train data: [70 71 72 73 74 75 76 77 78 79]\n",
      "Test data: [-0.62988799  0.31322878  0.96836446  0.73319032 -0.17607562]\n",
      "----------------------------------------\n",
      "Train data: [71 72 73 74 75 76 77 78 79 80]\n",
      "Test data: [ 0.31322878  0.96836446  0.73319032 -0.17607562 -0.92345845]\n",
      "----------------------------------------\n",
      "Train data: [72 73 74 75 76 77 78 79 80 81]\n",
      "Test data: [ 0.96836446  0.73319032 -0.17607562 -0.92345845 -0.82181784]\n",
      "----------------------------------------\n",
      "Train data: [73 74 75 76 77 78 79 80 81 82]\n",
      "Test data: [ 0.73319032 -0.17607562 -0.92345845 -0.82181784  0.0353983 ]\n",
      "----------------------------------------\n",
      "Train data: [74 75 76 77 78 79 80 81 82 83]\n",
      "Test data: [-0.17607562 -0.92345845 -0.82181784  0.0353983   0.86006941]\n",
      "----------------------------------------\n",
      "Train data: [75 76 77 78 79 80 81 82 83 84]\n",
      "Test data: [-0.92345845 -0.82181784  0.0353983   0.86006941  0.89399666]\n",
      "----------------------------------------\n",
      "Train data: [76 77 78 79 80 81 82 83 84 85]\n",
      "Test data: [-0.82181784  0.0353983   0.86006941  0.89399666  0.10598751]\n",
      "----------------------------------------\n",
      "Train data: [77 78 79 80 81 82 83 84 85 86]\n",
      "Test data: [ 0.0353983   0.86006941  0.89399666  0.10598751 -0.77946607]\n",
      "----------------------------------------\n",
      "Train data: [78 79 80 81 82 83 84 85 86 87]\n",
      "Test data: [ 0.86006941  0.89399666  0.10598751 -0.77946607 -0.94828214]\n",
      "----------------------------------------\n",
      "Train data: [79 80 81 82 83 84 85 86 87 88]\n",
      "Test data: [ 0.89399666  0.10598751 -0.77946607 -0.94828214 -0.24525199]\n",
      "----------------------------------------\n",
      "Train data: [80 81 82 83 84 85 86 87 88 89]\n",
      "Test data: [ 0.10598751 -0.77946607 -0.94828214 -0.24525199  0.68326171]\n",
      "----------------------------------------\n",
      "Train data: [81 82 83 84 85 86 87 88 89 90]\n",
      "Test data: [-0.77946607 -0.94828214 -0.24525199  0.68326171  0.98358775]\n",
      "----------------------------------------\n",
      "Train data: [82 83 84 85 86 87 88 89 90 91]\n",
      "Test data: [-0.94828214 -0.24525199  0.68326171  0.98358775  0.37960774]\n",
      "----------------------------------------\n",
      "Train data: [83 84 85 86 87 88 89 90 91 92]\n",
      "Test data: [-0.24525199  0.68326171  0.98358775  0.37960774 -0.57338187]\n",
      "----------------------------------------\n",
      "Train data: [84 85 86 87 88 89 90 91 92 93]\n",
      "Test data: [ 0.68326171  0.98358775  0.37960774 -0.57338187 -0.99920683]\n",
      "----------------------------------------\n",
      "Train data: [85 86 87 88 89 90 91 92 93 94]\n",
      "Test data: [ 0.98358775  0.37960774 -0.57338187 -0.99920683 -0.50636564]\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.model_selection import SlidingWindowSplitter\n",
    "import numpy as np\n",
    "\n",
    "# 生成示例时间序列数据\n",
    "time_series = np.arange(1, 101)\n",
    "target = np.sin(time_series)\n",
    "\n",
    "# 设置时间窗口参数，窗口大小为10，步长为5\n",
    "window_size = 10\n",
    "pred_len = 5\n",
    "\n",
    "# 创建 TimeSeriesSplit 对象\n",
    "tscv = SlidingWindowSplitter(window_length=window_size, fh=np.arange(pred_len)+1, start_with_window=True)\n",
    "\n",
    "# 利用 TimeSeriesSplit 进行迭代\n",
    "for train_idx, test_idx in tscv.split(target):\n",
    "    train_data = target[train_idx]\n",
    "    test_data = target[test_idx]\n",
    "\n",
    "    print(\"Train data:\", train_idx)\n",
    "    print(\"Test data:\", test_data)\n",
    "    print(\"-\" * 40)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T08:52:31.583797700Z",
     "start_time": "2023-08-08T08:52:31.581286800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "Prophet requires package 'prophet' to be present in the python environment, but 'prophet' was not found. 'prophet' is a soft dependency and not included in the base sktime installation. Please run: `pip install prophet` to install the prophet package. To install all soft dependencies, run: `pip install sktime[all_extras]`",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\utils\\validation\\_dependencies.py:138\u001B[0m, in \u001B[0;36m_check_soft_dependencies\u001B[1;34m(package_import_alias, severity, obj, msg, suppress_import_stdout, *packages)\u001B[0m\n\u001B[0;32m    137\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 138\u001B[0m         pkg_ref \u001B[38;5;241m=\u001B[39m \u001B[43mimport_module\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpackage_import_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    139\u001B[0m \u001B[38;5;66;03m# if package cannot be imported, make the user aware of installation requirement\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\importlib\\__init__.py:126\u001B[0m, in \u001B[0;36mimport_module\u001B[1;34m(name, package)\u001B[0m\n\u001B[0;32m    125\u001B[0m         level \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m--> 126\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_bootstrap\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_gcd_import\u001B[49m\u001B[43m(\u001B[49m\u001B[43mname\u001B[49m\u001B[43m[\u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpackage\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m<frozen importlib._bootstrap>:1206\u001B[0m, in \u001B[0;36m_gcd_import\u001B[1;34m(name, package, level)\u001B[0m\n",
      "File \u001B[1;32m<frozen importlib._bootstrap>:1178\u001B[0m, in \u001B[0;36m_find_and_load\u001B[1;34m(name, import_)\u001B[0m\n",
      "File \u001B[1;32m<frozen importlib._bootstrap>:1142\u001B[0m, in \u001B[0;36m_find_and_load_unlocked\u001B[1;34m(name, import_)\u001B[0m\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'prophet'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[7], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msktime\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforecasting\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mfbprophet\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Prophet\n\u001B[1;32m----> 2\u001B[0m forecaster \u001B[38;5;241m=\u001B[39m \u001B[43mProphet\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43mseasonality_mode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmultiplicative\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m    \u001B[49m\u001B[43mn_changepoints\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mint\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43my_train\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m/\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m12\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m    \u001B[49m\u001B[43madd_country_holidays\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcountry_name\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGermany\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      6\u001B[0m \u001B[43m    \u001B[49m\u001B[43myearly_seasonality\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m      7\u001B[0m \u001B[43m    \u001B[49m\u001B[43mweekly_seasonality\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m      8\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdaily_seasonality\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m      9\u001B[0m \u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\fbprophet.py:186\u001B[0m, in \u001B[0;36mProphet.__init__\u001B[1;34m(self, freq, add_seasonality, add_country_holidays, growth, growth_floor, growth_cap, changepoints, n_changepoints, changepoint_range, yearly_seasonality, weekly_seasonality, daily_seasonality, holidays, seasonality_mode, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, alpha, uncertainty_samples, stan_backend, verbose)\u001B[0m\n\u001B[0;32m    183\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstan_backend \u001B[38;5;241m=\u001B[39m stan_backend\n\u001B[0;32m    184\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mverbose \u001B[38;5;241m=\u001B[39m verbose\n\u001B[1;32m--> 186\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    188\u001B[0m \u001B[38;5;66;03m# import inside method to avoid hard dependency\u001B[39;00m\n\u001B[0;32m    189\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mprophet\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforecaster\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Prophet \u001B[38;5;28;01mas\u001B[39;00m _Prophet\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:115\u001B[0m, in \u001B[0;36mBaseForecaster.__init__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    112\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_converter_store_y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m()  \u001B[38;5;66;03m# storage dictionary for in/output conversion\u001B[39;00m\n\u001B[0;32m    114\u001B[0m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__init__\u001B[39m()\n\u001B[1;32m--> 115\u001B[0m \u001B[43m_check_estimator_deps\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\utils\\validation\\_dependencies.py:385\u001B[0m, in \u001B[0;36m_check_estimator_deps\u001B[1;34m(obj, msg, severity)\u001B[0m\n\u001B[0;32m    383\u001B[0m     pkg_deps \u001B[38;5;241m=\u001B[39m [pkg_deps]\n\u001B[0;32m    384\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m pkg_deps \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 385\u001B[0m     pkg_deps_ok \u001B[38;5;241m=\u001B[39m \u001B[43m_check_soft_dependencies\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    386\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mpkg_deps\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mseverity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mseverity\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpackage_import_alias\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpck_alias\u001B[49m\n\u001B[0;32m    387\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    388\u001B[0m     compatible \u001B[38;5;241m=\u001B[39m compatible \u001B[38;5;129;01mand\u001B[39;00m pkg_deps_ok\n\u001B[0;32m    390\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m compatible\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\utils\\validation\\_dependencies.py:163\u001B[0m, in \u001B[0;36m_check_soft_dependencies\u001B[1;34m(package_import_alias, severity, obj, msg, suppress_import_stdout, *packages)\u001B[0m\n\u001B[0;32m    159\u001B[0m \u001B[38;5;66;03m# if msg is not None, none of the above is executed,\u001B[39;00m\n\u001B[0;32m    160\u001B[0m \u001B[38;5;66;03m# so if msg is passed it overrides the default messages\u001B[39;00m\n\u001B[0;32m    162\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m severity \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124merror\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 163\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mModuleNotFoundError\u001B[39;00m(msg) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01me\u001B[39;00m\n\u001B[0;32m    164\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m severity \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwarning\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    165\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mwarn(msg, stacklevel\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m)\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: Prophet requires package 'prophet' to be present in the python environment, but 'prophet' was not found. 'prophet' is a soft dependency and not included in the base sktime installation. Please run: `pip install prophet` to install the prophet package. To install all soft dependencies, run: `pip install sktime[all_extras]`"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.fbprophet import Prophet\n",
    "forecaster = Prophet(\n",
    "    seasonality_mode=\"multiplicative\",\n",
    "    n_changepoints=int(len(y_train) / 12),\n",
    "    add_country_holidays={\"country_name\": \"Germany\"},\n",
    "    yearly_seasonality=True,\n",
    "    weekly_seasonality=False,\n",
    "    daily_seasonality=False,\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T07:56:11.090957400Z",
     "start_time": "2023-08-08T07:56:11.038151800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29]\n",
      "[30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
      " 54 55]\n"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
    "X = np.arange(1,56)\n",
    "X_train, X_test = temporal_train_test_split(X, test_size=26)\n",
    "print(X_train)\n",
    "print(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T09:14:44.478633700Z",
     "start_time": "2023-08-08T09:14:44.441124300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data: [1.83534919 1.83526253 1.83464551 1.83566166 1.83514614 1.82575679\n",
      " 1.82477385 1.82461327 1.82461955 1.81420194]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "(time) indices of series 0 are not contained in index of series 0, extra indices are: RangeIndex(start=8, stop=10, step=1)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[24], line 12\u001B[0m\n\u001B[0;32m     10\u001B[0m     test_data \u001B[38;5;241m=\u001B[39m target[test_idx]\n\u001B[0;32m     11\u001B[0m     \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTrain data:\u001B[39m\u001B[38;5;124m\"\u001B[39m, train_data\u001B[38;5;241m.\u001B[39mvalues)\n\u001B[1;32m---> 12\u001B[0m     \u001B[43mforecaster\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtrain_data\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtest_data\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;66;03m# forecaster = AutoARIMA()\u001B[39;00m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;66;03m#\u001B[39;00m\n\u001B[0;32m     15\u001B[0m \u001B[38;5;66;03m#\u001B[39;00m\n\u001B[0;32m     16\u001B[0m \u001B[38;5;66;03m#\u001B[39;00m\n\u001B[0;32m     17\u001B[0m \u001B[38;5;66;03m# y_pred = forecaster.predict(fh=fh, X=X_test)\u001B[39;00m\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:334\u001B[0m, in \u001B[0;36mBaseForecaster.fit\u001B[1;34m(self, y, X, fh)\u001B[0m\n\u001B[0;32m    331\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreset()\n\u001B[0;32m    333\u001B[0m \u001B[38;5;66;03m# check and convert X/y\u001B[39;00m\n\u001B[1;32m--> 334\u001B[0m X_inner, y_inner \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_check_X_y\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43my\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    336\u001B[0m \u001B[38;5;66;03m# set internal X/y to the new X/y\u001B[39;00m\n\u001B[0;32m    337\u001B[0m \u001B[38;5;66;03m# this also updates cutoff from y\u001B[39;00m\n\u001B[0;32m    338\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_update_y_X(y_inner, X_inner)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\forecasting\\base\\_base.py:1525\u001B[0m, in \u001B[0;36mBaseForecaster._check_X_y\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m   1521\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_tag(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mX-y-must-have-same-index\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m   1522\u001B[0m     \u001B[38;5;66;03m# currently, check_equal_time_index only works for Series\u001B[39;00m\n\u001B[0;32m   1523\u001B[0m     \u001B[38;5;66;03m# todo: fix this so the check is general, using get_time_index\u001B[39;00m\n\u001B[0;32m   1524\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_tag(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignores-exogeneous-X\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m X_scitype \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSeries\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m-> 1525\u001B[0m         \u001B[43mcheck_equal_time_index\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcontains\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1527\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_scitype \u001B[38;5;241m!=\u001B[39m X_scitype:\n\u001B[0;32m   1528\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mX and y must have the same scitype\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[1;32mE:\\RUL_venv\\Lib\\site-packages\\sktime\\utils\\validation\\series.py:284\u001B[0m, in \u001B[0;36mcheck_equal_time_index\u001B[1;34m(mode, *ys)\u001B[0m\n\u001B[0;32m    281\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmode must be \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mequal\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m or \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcontains\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    283\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m failure_cond:\n\u001B[1;32m--> 284\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(msg)\n",
      "\u001B[1;31mValueError\u001B[0m: (time) indices of series 0 are not contained in index of series 0, extra indices are: RangeIndex(start=8, stop=10, step=1)"
     ]
    }
   ],
   "source": [
    "\n",
    "target = raw_data.iloc[:, 0].dropna()\n",
    "tscv = SlidingWindowSplitter(window_length=10, fh=np.arange(8)+1, start_with_window=True)\n",
    "\n",
    "# 使得AutoARIMA输入10步预测8步\n",
    "forecaster = AutoARIMA(start_P=10, suppress_warnings=True)\n",
    "\n",
    "# 利用 TimeSeriesSplit 进行迭代\n",
    "for train_idx, test_idx in tscv.split(target):\n",
    "    train_data = target[train_idx]\n",
    "    test_data = target[test_idx]\n",
    "    print(\"Train data:\", train_data.values)\n",
    "    forecaster.fit(y=train_data.values, X=test_data.values)\n",
    "# forecaster = AutoARIMA()\n",
    "#\n",
    "#\n",
    "#\n",
    "# y_pred = forecaster.predict(fh=fh, X=X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T09:29:58.139281500Z",
     "start_time": "2023-08-08T09:29:58.098390100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ForecastingHorizon(['1958-01', '1958-02', '1958-03', '1958-04', '1958-05', '1958-06',\n",
      "             '1958-07', '1958-08', '1958-09', '1958-10', '1958-11', '1958-12',\n",
      "             '1959-01', '1959-02', '1959-03', '1959-04', '1959-05', '1959-06',\n",
      "             '1959-07', '1959-08', '1959-09', '1959-10', '1959-11', '1959-12',\n",
      "             '1960-01', '1960-02', '1960-03', '1960-04', '1960-05', '1960-06',\n",
      "             '1960-07', '1960-08', '1960-09', '1960-10', '1960-11', '1960-12'],\n",
      "            dtype='period[M]', name='Period', is_relative=False)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'ForecastingHorizon' object has no attribute 'n_periods'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[26], line 13\u001B[0m\n\u001B[0;32m     11\u001B[0m fh \u001B[38;5;241m=\u001B[39m ForecastingHorizon(y_test\u001B[38;5;241m.\u001B[39mindex, is_relative\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m     12\u001B[0m \u001B[38;5;28mprint\u001B[39m(fh)\n\u001B[1;32m---> 13\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[43mfh\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mn_periods\u001B[49m)\n",
      "\u001B[1;31mAttributeError\u001B[0m: 'ForecastingHorizon' object has no attribute 'n_periods'"
     ]
    }
   ],
   "source": [
    "# imports necessary for this chapter\n",
    "from sktime.datasets import load_airline\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "# data loading for illustration (see section 1 for explanation)\n",
    "y = load_airline()\n",
    "y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
    "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
    "print(fh)\n",
    "print(fh.n_periods)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T10:58:26.588476700Z",
     "start_time": "2023-08-08T10:58:26.554756300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}