{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e4793ef5",
   "metadata": {},
   "source": [
    "# Ensembles\n",
    "\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/203-ensembles.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4949ce7f",
   "metadata": {},
   "source": [
    "This notebook contains the simple examples of using the ensemble models with ETNA library.\n",
    "\n",
    "**Table of contents**\n",
    "\n",
    "* [Loading dataset](#chapter1)  \n",
    "* [Building pipelines](#chapter2)\n",
    "* [Ensembles](#chapter3)\n",
    "    * [VotingEnsemble](#section_3_1)\n",
    "    * [StackingEnsamble](#section_3_2)\n",
    "    * [Results](#section_3_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b9df4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYTHONWARNINGS=ignore::FutureWarning\n"
     ]
    }
   ],
   "source": [
    "%env PYTHONWARNINGS=ignore::FutureWarning\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37e118e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from etna.datasets import TSDataset\n",
    "from etna.metrics import MAE\n",
    "from etna.metrics import MAPE\n",
    "from etna.metrics import MSE\n",
    "from etna.metrics import SMAPE\n",
    "from etna.models import CatBoostMultiSegmentModel\n",
    "from etna.models import NaiveModel\n",
    "from etna.models import SeasonalMovingAverageModel\n",
    "from etna.pipeline import Pipeline\n",
    "from etna.transforms import LagTransform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82360d8",
   "metadata": {},
   "source": [
    "## 1. Loading dataset <a class=\"anchor\" id=\"chapter1\"></a>\n",
    "\n",
    "In this notebook we will work with the dataset contains only one segment with monthly wine sales. Working process with the dataset containing more segments will be absolutely the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01e2fcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/monthly-australian-wine-sales.csv\")\n",
    "df[\"timestamp\"] = pd.to_datetime(df[\"month\"])\n",
    "df[\"target\"] = df[\"sales\"]\n",
    "df.drop(columns=[\"month\", \"sales\"], inplace=True)\n",
    "df[\"segment\"] = \"main\"\n",
    "ts = TSDataset(df=df, freq=\"MS\")\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c879183c",
   "metadata": {},
   "source": [
    "## 2. Building pipelines <a class=\"anchor\" id=\"chapter2\"></a>\n",
    "\n",
    "Given the sales' history, we want to select the best model(pipeline) to forecast future sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ee58fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "HORIZON = 3\n",
    "N_FOLDS = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6815f49",
   "metadata": {},
   "source": [
    "Let's build four pipelines using the different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0dc26e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "naive_pipeline = Pipeline(model=NaiveModel(lag=12), transforms=[], horizon=HORIZON)\n",
    "seasonalma_pipeline = Pipeline(\n",
    "    model=SeasonalMovingAverageModel(window=5, seasonality=12),\n",
    "    transforms=[],\n",
    "    horizon=HORIZON,\n",
    ")\n",
    "catboost_pipeline = Pipeline(\n",
    "    model=CatBoostMultiSegmentModel(),\n",
    "    transforms=[LagTransform(lags=[6, 7, 8, 9, 10, 11, 12], in_column=\"target\")],\n",
    "    horizon=HORIZON,\n",
    ")\n",
    "pipeline_names = [\"naive\", \"moving average\", \"catboost\"]\n",
    "pipelines = [naive_pipeline, seasonalma_pipeline, catboost_pipeline]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106e3885",
   "metadata": {},
   "source": [
    "And evaluate their performance on the backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53c1a0b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   15.2s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   23.7s remaining:   35.5s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   34.3s remaining:   22.8s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   58.7s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.0s remaining:    0.1s\n",
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      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   24.7s remaining:   37.0s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   36.1s remaining:   24.1s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:  1.1min finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
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      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   16.2s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   30.4s remaining:   45.5s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   44.6s remaining:   29.7s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.1s remaining:    0.1s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:    0.1s remaining:    0.1s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
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      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "metrics = []\n",
    "for pipeline in pipelines:\n",
    "    metrics.append(\n",
    "        pipeline.backtest(\n",
    "            ts=ts,\n",
    "            metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "            n_folds=N_FOLDS,\n",
    "            aggregate_metrics=True,\n",
    "            n_jobs=5,\n",
    "        )[0].iloc[:, 1:]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "928e04bd",
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive</th>\n",
       "      <td>2437.466667</td>\n",
       "      <td>1.089199e+07</td>\n",
       "      <td>9.949886</td>\n",
       "      <td>10.222106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moving average</th>\n",
       "      <td>1913.826667</td>\n",
       "      <td>6.113701e+06</td>\n",
       "      <td>7.897570</td>\n",
       "      <td>7.824056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>2271.766726</td>\n",
       "      <td>8.923741e+06</td>\n",
       "      <td>9.376638</td>\n",
       "      <td>10.013138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        MAE           MSE     SMAPE       MAPE\n",
       "naive           2437.466667  1.089199e+07  9.949886  10.222106\n",
       "moving average  1913.826667  6.113701e+06  7.897570   7.824056\n",
       "catboost        2271.766726  8.923741e+06  9.376638  10.013138"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.concat(metrics)\n",
    "metrics.index = pipeline_names\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9581f2a",
   "metadata": {},
   "source": [
    "## 3. Ensembles <a class=\"anchor\" id=\"chapter3\"></a>\n",
    "To improve the performance of the individual models, we can try to make ensembles out of them. Our library contains two ensembling methods, which we will try on now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e7e3e6",
   "metadata": {},
   "source": [
    "### 3.1 `VotingEnsemble` <a class=\"anchor\" id=\"section_3_1\"></a>\n",
    "\n",
    "`VotingEnsemble` forecasts future values with weighted averaging of it's `pipelines` forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5338aeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.ensembles import VotingEnsemble"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7ee7db",
   "metadata": {},
   "source": [
    "By default, `VotingEnsemble` uses **uniform** weights for the pipelines' forecasts. However, you can specify the weights manually using the `weights` parameter. The higher weight the more you trust the base model. In addition, you can set `weights` with the literal `auto`. In this case, the weights of pipelines are assigned with the importances got from `feature_importance_` property of `regressor`.\n",
    "\n",
    "*Note*: The `weights` are automatically normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c4029fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_ensemble = VotingEnsemble(pipelines=pipelines, weights=[1, 9, 4], n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1cb83b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "[Parallel(n_jobs=4)]: Using backend MultiprocessingBackend with 4 concurrent workers.\n",
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     ]
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>voting ensemble</th>\n",
       "      <td>1972.207943</td>\n",
       "      <td>6.685831e+06</td>\n",
       "      <td>8.172377</td>\n",
       "      <td>8.299714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         MAE           MSE     SMAPE      MAPE\n",
       "voting ensemble  1972.207943  6.685831e+06  8.172377  8.299714"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_ensamble_metrics = voting_ensemble.backtest(\n",
    "    ts=ts,\n",
    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "    n_folds=N_FOLDS,\n",
    "    aggregate_metrics=True,\n",
    "    n_jobs=1,\n",
    ")[0].iloc[:, 1:]\n",
    "voting_ensamble_metrics.index = [\"voting ensemble\"]\n",
    "voting_ensamble_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26b503b",
   "metadata": {},
   "source": [
    "### 3.2 `StackingEnsemble` <a class=\"anchor\" id=\"section_3_2\"></a>\n",
    "`StackingEnsemble` forecasts future using the metamodel to combine the forecasts of it's `pipelines`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78c46663",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.ensembles import StackingEnsemble"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b430668",
   "metadata": {},
   "source": [
    "By default, `StackingEnsemble` uses only the pipelines' forecasts as features for the `final_model`. However, you can specify the additional features using the `features_to_use` parameter. The following values are possible:\n",
    "\n",
    "+ **None** - use only the pipelines' forecasts(default)\n",
    "+ **List[str]** - use the pipelines' forecasts + features from the list\n",
    "+ **\"all\"** - use all the available features\n",
    "\n",
    "*Note:* It is possible to use only the features available for the base models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "273626b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "stacking_ensemble_unfeatured = StackingEnsemble(pipelines=pipelines, n_folds=10, n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "272cc433",
   "metadata": {},
   "outputs": [
    {
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      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   20.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:   51.5s finished\n",
      "[Parallel(n_jobs=4)]: Using backend MultiprocessingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   17.8s\n",
      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:   49.4s finished\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=4)]: Using backend MultiprocessingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   11.7s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:   41.2s finished\n",
      "[Parallel(n_jobs=4)]: Using backend MultiprocessingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   15.2s\n",
      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:   41.1s finished\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=4)]: Using backend MultiprocessingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   1 tasks      | elapsed:   17.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done   2 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=1)]: Done   3 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    1.4s\n",
      "[Parallel(n_jobs=1)]: Done   5 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=1)]: Done   6 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=1)]: Done   8 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=1)]: Done  10 tasks      | elapsed:    3.8s\n",
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stacking ensemble</th>\n",
       "      <td>1986.453478</td>\n",
       "      <td>7.309679e+06</td>\n",
       "      <td>8.276998</td>\n",
       "      <td>8.328746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MAE           MSE     SMAPE      MAPE\n",
       "stacking ensemble  1986.453478  7.309679e+06  8.276998  8.328746"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_ensamble_metrics = stacking_ensemble_unfeatured.backtest(\n",
    "    ts=ts,\n",
    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "    n_folds=N_FOLDS,\n",
    "    aggregate_metrics=True,\n",
    "    n_jobs=1,\n",
    ")[0].iloc[:, 1:]\n",
    "stacking_ensamble_metrics.index = [\"stacking ensemble\"]\n",
    "stacking_ensamble_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051a0ba0",
   "metadata": {},
   "source": [
    "In addition, it is also possible to specify the `final_model`. You can use any regression model with the sklearn interface for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c975d5c5",
   "metadata": {},
   "source": [
    "### 3.3 Results<a class=\"anchor\" id=\"section_3_3\"></a>\n",
    "\n",
    "Finally, let's take a look at the results of our experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2f1d397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive</th>\n",
       "      <td>2437.466667</td>\n",
       "      <td>1.089199e+07</td>\n",
       "      <td>9.949886</td>\n",
       "      <td>10.222106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moving average</th>\n",
       "      <td>1913.826667</td>\n",
       "      <td>6.113701e+06</td>\n",
       "      <td>7.897570</td>\n",
       "      <td>7.824056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>2271.766726</td>\n",
       "      <td>8.923741e+06</td>\n",
       "      <td>9.376638</td>\n",
       "      <td>10.013138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voting ensemble</th>\n",
       "      <td>1972.207943</td>\n",
       "      <td>6.685831e+06</td>\n",
       "      <td>8.172377</td>\n",
       "      <td>8.299714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stacking ensemble</th>\n",
       "      <td>1986.453478</td>\n",
       "      <td>7.309679e+06</td>\n",
       "      <td>8.276998</td>\n",
       "      <td>8.328746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MAE           MSE     SMAPE       MAPE\n",
       "naive              2437.466667  1.089199e+07  9.949886  10.222106\n",
       "moving average     1913.826667  6.113701e+06  7.897570   7.824056\n",
       "catboost           2271.766726  8.923741e+06  9.376638  10.013138\n",
       "voting ensemble    1972.207943  6.685831e+06  8.172377   8.299714\n",
       "stacking ensemble  1986.453478  7.309679e+06  8.276998   8.328746"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.concat([metrics, voting_ensamble_metrics, stacking_ensamble_metrics])\n",
    "metrics"
   ]
  }
 ],
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