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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import kagglehub\n",
    "\n",
    "from abc import ABC\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error as mape\n",
    "\n",
    "from statsforecast.models import AutoTheta, AutoARIMA, AutoETS\n",
    "from statsforecast import StatsForecast\n",
    "from utilsforecast.plotting import plot_series\n",
    "\n",
    "from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Download information from Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = kagglehub.dataset_download(\n",
    "    \"rickandjoe/electricity-transformer-dataset-etdataset\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Load the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(path + \"/ETT-small/ETTh1.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"unique_id\"] = \"transformer_1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(path + \"/ETT-small/ETTh2.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[\"unique_id\"] = \"transformer_2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate both dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Drop useless information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final[[\"unique_id\", \"OT\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Rename the DF to get the final prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Rename the columns to have a common format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.rename(columns={\"date\": \"ds\", \"OT\": \"y\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"ds\"] = pd.to_datetime(df_final[\"ds\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.sort_values([\"unique_id\", \"ds\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>transformer_1</td>\n",
       "      <td>30.531000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>transformer_1</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>transformer_1</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>transformer_1</td>\n",
       "      <td>25.044001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>transformer_1</td>\n",
       "      <td>21.948000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34835</th>\n",
       "      <td>2018-06-26 15:00:00</td>\n",
       "      <td>transformer_2</td>\n",
       "      <td>47.084999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34836</th>\n",
       "      <td>2018-06-26 16:00:00</td>\n",
       "      <td>transformer_2</td>\n",
       "      <td>48.183498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34837</th>\n",
       "      <td>2018-06-26 17:00:00</td>\n",
       "      <td>transformer_2</td>\n",
       "      <td>48.183498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34838</th>\n",
       "      <td>2018-06-26 18:00:00</td>\n",
       "      <td>transformer_2</td>\n",
       "      <td>46.865501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34839</th>\n",
       "      <td>2018-06-26 19:00:00</td>\n",
       "      <td>transformer_2</td>\n",
       "      <td>45.986500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34840 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ds      unique_id          y\n",
       "0     2016-07-01 00:00:00  transformer_1  30.531000\n",
       "1     2016-07-01 01:00:00  transformer_1  27.787001\n",
       "2     2016-07-01 02:00:00  transformer_1  27.787001\n",
       "3     2016-07-01 03:00:00  transformer_1  25.044001\n",
       "4     2016-07-01 04:00:00  transformer_1  21.948000\n",
       "...                   ...            ...        ...\n",
       "34835 2018-06-26 15:00:00  transformer_2  47.084999\n",
       "34836 2018-06-26 16:00:00  transformer_2  48.183498\n",
       "34837 2018-06-26 17:00:00  transformer_2  48.183498\n",
       "34838 2018-06-26 18:00:00  transformer_2  46.865501\n",
       "34839 2018-06-26 19:00:00  transformer_2  45.986500\n",
       "\n",
       "[34840 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date-Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a variable for the split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPLIT_DATE = \"2018-06-22 20:00:00\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df_final[df_final[\"ds\"] < SPLIT_DATE]\n",
    "test = df_final[df_final[\"ds\"] >= SPLIT_DATE]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an abstract class the handles all the model functionalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForecastingBaseModel(ABC):\n",
    "    def __init__(self, freq: str, n_jobs: int = 1) -> None:\n",
    "        self.model = None\n",
    "        self.freq = freq\n",
    "        self.n_jobs = n_jobs\n",
    "\n",
    "    def fit(\n",
    "        self, df: pd.DataFrame, date_col: str, item_col: str, targe_col: str\n",
    "    ) -> None:\n",
    "        pass\n",
    "\n",
    "    def predict(self, n_steps: int) -> pd.DataFrame:\n",
    "        pass\n",
    "\n",
    "    def evaluate_model(self, y_true: np.array, y_pred: np.array) -> None:\n",
    "        print(\"MAPE: \", mape(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a model class for the StatsModels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StatsForecaster(ForecastingBaseModel):\n",
    "    def __init__(self, freq: str = \"H\", n_jobs: int = -1) -> None:\n",
    "        super().__init__(freq=freq, n_jobs=n_jobs)\n",
    "\n",
    "    def fit(\n",
    "        self, df: pd.DataFrame, date_col: str, item_col: str, target_col: str\n",
    "    ) -> None:\n",
    "        self.df = df.copy()\n",
    "        self.date_col = date_col\n",
    "        self.model = StatsForecast(\n",
    "            models=[AutoARIMA(), AutoETS(), AutoTheta()],\n",
    "            freq=self.freq,\n",
    "            n_jobs=self.n_jobs,\n",
    "        )\n",
    "        self.model.fit(\n",
    "            df=self.df, time_col=date_col, target_col=target_col, id_col=item_col\n",
    "        )\n",
    "\n",
    "    def predict(self, n_steps: int) -> pd.DataFrame:\n",
    "        results = self.model.predict(h=n_steps)\n",
    "        results = results.rename(columns={self.date_col: \"ds\"})\n",
    "        return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a class for the AWS Chronos model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChronosForecaster(ForecastingBaseModel):\n",
    "    def __init__(self, freq: str = \"H\"):\n",
    "        super().__init__(freq=freq)\n",
    "\n",
    "    def fit(\n",
    "        self, df: pd.DataFrame, date_col: str, item_col: str, target_col: str\n",
    "    ) -> None:\n",
    "        self.item_id = item_col\n",
    "        df = df.copy()\n",
    "        df = df.rename(columns={target_col: \"target\"})\n",
    "        self.df = TimeSeriesDataFrame.from_data_frame(\n",
    "            df,\n",
    "            id_column=item_col,\n",
    "            timestamp_column=date_col,\n",
    "        )\n",
    "\n",
    "    def predict(self, n_steps):\n",
    "        self.model = TimeSeriesPredictor(\n",
    "            prediction_length=n_steps, freq=self.freq, verbosity=0\n",
    "        ).fit(self.df, presets=\"bolt_base\")\n",
    "        results = self.model.predict(self.df)\n",
    "        results = results.to_data_frame().reset_index()\n",
    "        results = results[[\"mean\", \"item_id\", \"timestamp\"]]\n",
    "        results = results.rename(\n",
    "            columns={\n",
    "                \"mean\": \"AWSChronosForecast\",\n",
    "                \"item_id\": \"unique_id\",\n",
    "                \"timestamp\": \"ds\",\n",
    "            }\n",
    "        )\n",
    "        return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "chronos_model = ChronosForecaster(freq=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "chronos_model.fit(df=train, date_col=\"ds\", item_col=\"unique_id\", target_col=\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_model = StatsForecaster(freq=\"h\", n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_model.fit(df=train, date_col=\"ds\", item_col=\"unique_id\", target_col=\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the horizon lenght"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "HORIZON_LEN = 96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_chronos = chronos_model.predict(n_steps=HORIZON_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_stats = stats_model.predict(n_steps=HORIZON_LEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_final = pd.merge(\n",
    "    y_pred_chronos, y_pred_stats, on=[\"unique_id\", \"ds\"], how=\"inner\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(df_final.query('ds > \"2018-06-01\"'), y_pred_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function that can perform those calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForecastEvaluation:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def evaluate(\n",
    "        self, df: pd.DataFrame, predicted_cols: list, y_true: np.array\n",
    "    ) -> None:\n",
    "        mape_results = {\n",
    "            col: mape(y_true=y_true, y_pred=df[col].values) * 100\n",
    "            for col in predicted_cols\n",
    "        }\n",
    "        return pd.DataFrame([mape_results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_evaluator = ForecastEvaluation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mape_results = forecast_evaluator.evaluate(\n",
    "    df=y_pred_final,\n",
    "    predicted_cols=[\"AWSChronosForecast\", \"AutoARIMA\", \"AutoETS\", \"AutoTheta\"],\n",
    "    y_true=test[\"y\"].values,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AWSChronosForecast</th>\n",
       "      <th>AutoARIMA</th>\n",
       "      <th>AutoETS</th>\n",
       "      <th>AutoTheta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.148477</td>\n",
       "      <td>18.487053</td>\n",
       "      <td>27.713882</td>\n",
       "      <td>27.880458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AWSChronosForecast  AutoARIMA    AutoETS  AutoTheta\n",
       "0            9.148477  18.487053  27.713882  27.880458"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aws_conf",
   "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": "3.11.13"
  }
 },
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 "nbformat_minor": 2
}