sklearn_time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82e1459a",
   "metadata": {},
   "source": [
    "# Time Series Forecasting with *scikit-learn* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30be1e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abce7426",
   "metadata": {},
   "source": [
    "## 1. Get the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3f3937f",
   "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>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sm.datasets.co2.load_pandas().data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9e1a650",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 11))\n",
    "\n",
    "ax.plot(data['co2'])\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('CO2 concentration (ppmw)')\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ada2472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "co2    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.interpolate()\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12d582c",
   "metadata": {},
   "source": [
    "## Predict the next step using the last observation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "383c55fa",
   "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>co2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-01</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-08</th>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-15</th>\n",
       "      <td>371.2</td>\n",
       "      <td>371.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>371.3</td>\n",
       "      <td>371.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-29</th>\n",
       "      <td>371.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2284 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2      y\n",
       "1958-03-29  316.1  317.3\n",
       "1958-04-05  317.3  317.6\n",
       "1958-04-12  317.6  317.5\n",
       "1958-04-19  317.5  316.4\n",
       "1958-04-26  316.4  316.9\n",
       "...           ...    ...\n",
       "2001-12-01  370.3  370.8\n",
       "2001-12-08  370.8  371.2\n",
       "2001-12-15  371.2  371.3\n",
       "2001-12-22  371.3  371.5\n",
       "2001-12-29  371.5    NaN\n",
       "\n",
       "[2284 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "\n",
    "df['y'] = df['co2'].shift(-1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0351772e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set: (2180, 2)\n",
      "Shape of test set: (104, 2)\n"
     ]
    }
   ],
   "source": [
    "train = df[:-104]\n",
    "test = df[-104:]\n",
    "\n",
    "print(f'Shape of training set: {train.shape}')\n",
    "print(f'Shape of test set: {test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0c1ba4",
   "metadata": {},
   "source": [
    "### Baseline model: repeat the last observation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cd44030",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co2</th>\n",
       "      <th>y</th>\n",
       "      <th>baseline_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>368.5</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>369.0</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>369.8</td>\n",
       "      <td>369.2</td>\n",
       "      <td>369.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>369.2</td>\n",
       "      <td>369.1</td>\n",
       "      <td>369.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-05</th>\n",
       "      <td>369.1</td>\n",
       "      <td>369.6</td>\n",
       "      <td>369.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-24</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-01</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>370.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-08</th>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "      <td>370.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-15</th>\n",
       "      <td>371.2</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>371.3</td>\n",
       "      <td>371.5</td>\n",
       "      <td>371.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2      y  baseline_pred\n",
       "2000-01-08  368.5  369.0          368.5\n",
       "2000-01-15  369.0  369.8          369.0\n",
       "2000-01-22  369.8  369.2          369.8\n",
       "2000-01-29  369.2  369.1          369.2\n",
       "2000-02-05  369.1  369.6          369.1\n",
       "...           ...    ...            ...\n",
       "2001-11-24  370.3  370.3          370.3\n",
       "2001-12-01  370.3  370.8          370.3\n",
       "2001-12-08  370.8  371.2          370.8\n",
       "2001-12-15  371.2  371.3          371.2\n",
       "2001-12-22  371.3  371.5          371.3\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.copy()\n",
    "test['baseline_pred'] = test['co2']\n",
    "\n",
    "test = test.drop(test.tail(1).index) # Drop last row\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf21408",
   "metadata": {},
   "source": [
    "### Apply a Decision Tree (can be replaced with any regressor model in sklearn) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac3a8cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "X_train = train['co2'].values.reshape(-1,1)\n",
    "y_train = train['y'].values.reshape(-1,1)\n",
    "X_test = test['co2'].values.reshape(-1,1)\n",
    "\n",
    "# Initialize the model\n",
    "dt_reg = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "dt_reg.fit(X=X_train, y=y_train)\n",
    "\n",
    "# Make predictions\n",
    "dt_pred = dt_reg.predict(X_test)\n",
    "\n",
    "# Assign predictions to a new column in test\n",
    "test['dt_pred'] = dt_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec0d6c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>co2</th>\n",
       "      <th>y</th>\n",
       "      <th>baseline_pred</th>\n",
       "      <th>dt_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>368.5</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.5</td>\n",
       "      <td>368.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>369.0</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>369.8</td>\n",
       "      <td>369.2</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>369.2</td>\n",
       "      <td>369.1</td>\n",
       "      <td>369.2</td>\n",
       "      <td>369.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-05</th>\n",
       "      <td>369.1</td>\n",
       "      <td>369.6</td>\n",
       "      <td>369.1</td>\n",
       "      <td>368.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-24</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-01</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-08</th>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "      <td>370.8</td>\n",
       "      <td>370.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-15</th>\n",
       "      <td>371.2</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.2</td>\n",
       "      <td>371.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>371.3</td>\n",
       "      <td>371.5</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2      y  baseline_pred     dt_pred\n",
       "2000-01-08  368.5  369.0          368.5  368.650000\n",
       "2000-01-15  369.0  369.8          369.0  368.500000\n",
       "2000-01-22  369.8  369.2          369.8  369.400000\n",
       "2000-01-29  369.2  369.1          369.2  369.100000\n",
       "2000-02-05  369.1  369.6          369.1  368.800000\n",
       "...           ...    ...            ...         ...\n",
       "2001-11-24  370.3  370.3          370.3  370.533333\n",
       "2001-12-01  370.3  370.8          370.3  370.533333\n",
       "2001-12-08  370.8  371.2          370.8  370.800000\n",
       "2001-12-15  371.2  371.3          371.2  371.000000\n",
       "2001-12-22  371.3  371.5          371.3  371.000000\n",
       "\n",
       "[103 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e887cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape(y_true, y_pred):\n",
    "    return round(np.mean(np.abs((y_true - y_pred) / y_true)) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "782cfd6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 0.1%\n",
      "Decision Tree: 0.21%\n"
     ]
    }
   ],
   "source": [
    "baseline_mape = mape(test['y'], test['baseline_pred'])\n",
    "dt_mape = mape(test['y'], test['dt_pred'])\n",
    "\n",
    "print(f'Baseline: {baseline_mape}%')\n",
    "print(f'Decision Tree: {dt_mape}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc7bb657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>co2</th>\n",
       "      <th>y</th>\n",
       "      <th>baseline_pred</th>\n",
       "      <th>dt_pred</th>\n",
       "      <th>gbr_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>368.5</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.5</td>\n",
       "      <td>368.650000</td>\n",
       "      <td>368.556464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>369.0</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>368.774435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>369.8</td>\n",
       "      <td>369.2</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.400000</td>\n",
       "      <td>369.512468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>369.2</td>\n",
       "      <td>369.1</td>\n",
       "      <td>369.2</td>\n",
       "      <td>369.100000</td>\n",
       "      <td>369.041813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-05</th>\n",
       "      <td>369.1</td>\n",
       "      <td>369.6</td>\n",
       "      <td>369.1</td>\n",
       "      <td>368.800000</td>\n",
       "      <td>368.774435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-24</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.533333</td>\n",
       "      <td>370.535661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-01</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.533333</td>\n",
       "      <td>370.535661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-08</th>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "      <td>370.8</td>\n",
       "      <td>370.800000</td>\n",
       "      <td>370.710834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-15</th>\n",
       "      <td>371.2</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.2</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>371.049344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>371.3</td>\n",
       "      <td>371.5</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>371.049344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2      y  baseline_pred     dt_pred    gbr_pred\n",
       "2000-01-08  368.5  369.0          368.5  368.650000  368.556464\n",
       "2000-01-15  369.0  369.8          369.0  368.500000  368.774435\n",
       "2000-01-22  369.8  369.2          369.8  369.400000  369.512468\n",
       "2000-01-29  369.2  369.1          369.2  369.100000  369.041813\n",
       "2000-02-05  369.1  369.6          369.1  368.800000  368.774435\n",
       "...           ...    ...            ...         ...         ...\n",
       "2001-11-24  370.3  370.3          370.3  370.533333  370.535661\n",
       "2001-12-01  370.3  370.8          370.3  370.533333  370.535661\n",
       "2001-12-08  370.8  371.2          370.8  370.800000  370.710834\n",
       "2001-12-15  371.2  371.3          371.2  371.000000  371.049344\n",
       "2001-12-22  371.3  371.5          371.3  371.000000  371.049344\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbr = GradientBoostingRegressor(random_state=42)\n",
    "\n",
    "gbr.fit(X_train, y=y_train.ravel())\n",
    "\n",
    "gbr_pred = gbr.predict(X_test)\n",
    "\n",
    "test['gbr_pred'] = gbr_pred\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c5945d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regressor: 0.15%\n"
     ]
    }
   ],
   "source": [
    "gbr_mape = mape(test['co2'], test['gbr_pred'])\n",
    "\n",
    "print(f'Gradient Boosting Regressor: {gbr_mape}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3ab0598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "x = ['Baseline', 'Decision Tree', 'Gradient Boosting']\n",
    "y = [baseline_mape, dt_mape, gbr_mape]\n",
    "\n",
    "ax.bar(x, y, width=0.4)\n",
    "ax.set_xlabel('Regressor models')\n",
    "ax.set_ylabel('MAPE (%)')\n",
    "ax.set_ylim(0, 0.3)\n",
    "\n",
    "for index, value in enumerate(y):\n",
    "    plt.text(x=index, y=value + 0.02, s=str(value), ha='center')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a6f91c",
   "metadata": {},
   "source": [
    "## Predict the next step with a window as input "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30b37252",
   "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>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-01</th>\n",
       "      <td>370.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-08</th>\n",
       "      <td>370.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-15</th>\n",
       "      <td>371.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>371.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-29</th>\n",
       "      <td>371.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2284 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4\n",
       "...           ...\n",
       "2001-12-01  370.3\n",
       "2001-12-08  370.8\n",
       "2001-12-15  371.2\n",
       "2001-12-22  371.3\n",
       "2001-12-29  371.5\n",
       "\n",
       "[2284 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a77017e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def window_input(window_length: int, data: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    df = data.copy()\n",
    "    \n",
    "    i = 1\n",
    "    while i < window_length:\n",
    "        df[f'x_{i}'] = df['co2'].shift(-i)\n",
    "        i = i + 1\n",
    "        \n",
    "    if i == window_length:\n",
    "        df['y'] = df['co2'].shift(-i)\n",
    "        \n",
    "    # Drop rows where there is a NaN\n",
    "    df = df.dropna(axis=0)\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16ffc4ca",
   "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>co2</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "      <td>317.3</td>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.5</td>\n",
       "      <td>317.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.5</td>\n",
       "      <td>317.9</td>\n",
       "      <td>317.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-27</th>\n",
       "      <td>368.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>368.8</td>\n",
       "      <td>369.7</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-03</th>\n",
       "      <td>368.7</td>\n",
       "      <td>368.8</td>\n",
       "      <td>369.7</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-10</th>\n",
       "      <td>368.8</td>\n",
       "      <td>369.7</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>371.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-17</th>\n",
       "      <td>369.7</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "      <td>371.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11-24</th>\n",
       "      <td>370.3</td>\n",
       "      <td>370.3</td>\n",
       "      <td>370.8</td>\n",
       "      <td>371.2</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2279 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2    x_1    x_2    x_3    x_4       y\n",
       "1958-03-29  316.1  317.3  317.6  317.5  316.4  316.90\n",
       "1958-04-05  317.3  317.6  317.5  316.4  316.9  317.20\n",
       "1958-04-12  317.6  317.5  316.4  316.9  317.2  317.50\n",
       "1958-04-19  317.5  316.4  316.9  317.2  317.5  317.90\n",
       "1958-04-26  316.4  316.9  317.2  317.5  317.9  317.55\n",
       "...           ...    ...    ...    ...    ...     ...\n",
       "2001-10-27  368.7  368.7  368.8  369.7  370.3  370.30\n",
       "2001-11-03  368.7  368.8  369.7  370.3  370.3  370.80\n",
       "2001-11-10  368.8  369.7  370.3  370.3  370.8  371.20\n",
       "2001-11-17  369.7  370.3  370.3  370.8  371.2  371.30\n",
       "2001-11-24  370.3  370.3  370.8  371.2  371.3  371.50\n",
       "\n",
       "[2279 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = window_input(5, data)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0225f7",
   "metadata": {},
   "source": [
    "### Baseline model: predict the mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c9ab739",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = new_df[['co2', 'x_1', 'x_2', 'x_3', 'x_4']].values\n",
    "y = new_df['y'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ba1863d",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_pred = []\n",
    "\n",
    "for row in X_test:\n",
    "    baseline_pred.append(np.mean(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9596c8",
   "metadata": {},
   "source": [
    "### Apply a decision tree (or any other model, really!) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eeb31351",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_reg_5 = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "dt_reg_5.fit(X_train, y_train)\n",
    "\n",
    "dt_reg_5_pred = dt_reg_5.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2edaf7a3",
   "metadata": {},
   "source": [
    "### Apply gradient boosting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "005ee3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr_5 = GradientBoostingRegressor(random_state=42)\n",
    "\n",
    "gbr_5.fit(X_train, y_train.ravel())\n",
    "\n",
    "gbr_5_pred = gbr_5.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "836b0b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline MAPE: 0.23%\n",
      "Decision Tree MAPE: 2.14%\n",
      "Gradient Boosting MAPE: 2.04%\n"
     ]
    }
   ],
   "source": [
    "baseline_mape = mape(y_test, baseline_pred)\n",
    "dt_5_mape = mape(y_test, dt_reg_5_pred)\n",
    "gbr_5_mape = mape(y_test, gbr_5_pred)\n",
    "\n",
    "print(f'Baseline MAPE: {baseline_mape}%')\n",
    "print(f'Decision Tree MAPE: {dt_5_mape}%')\n",
    "print(f'Gradient Boosting MAPE: {gbr_5_mape}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fda19f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "x = ['Baseline', 'Decision Tree', 'Gradient Boosting']\n",
    "y = [baseline_mape, dt_5_mape, gbr_5_mape]\n",
    "\n",
    "ax.bar(x, y, width=0.4)\n",
    "ax.set_xlabel('Regressor models')\n",
    "ax.set_ylabel('MAPE (%)')\n",
    "ax.set_ylim(0, 2.5)\n",
    "\n",
    "for index, value in enumerate(y):\n",
    "    plt.text(x=index, y=value + 0.1, s=str(value), ha='center')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f875672c",
   "metadata": {},
   "source": [
    "## Predict a sequence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d4122a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def window_input_output(input_length: int, output_length: int, data: pd.DataFrame) -> pd.DataFrame:\n",
    "    \n",
    "    df = data.copy()\n",
    "    \n",
    "    i = 1\n",
    "    while i < input_length:\n",
    "        df[f'x_{i}'] = df['co2'].shift(-i)\n",
    "        i = i + 1\n",
    "        \n",
    "    j = 0\n",
    "    while j < output_length:\n",
    "        df[f'y_{j}'] = df['co2'].shift(-output_length-j)\n",
    "        j = j + 1\n",
    "        \n",
    "    df = df.dropna(axis=0)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e947e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_df = window_input_output(26, 26, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "82d4d98c",
   "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>co2</th>\n",
       "      <th>x_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>x_3</th>\n",
       "      <th>x_4</th>\n",
       "      <th>x_5</th>\n",
       "      <th>x_6</th>\n",
       "      <th>x_7</th>\n",
       "      <th>x_8</th>\n",
       "      <th>x_9</th>\n",
       "      <th>...</th>\n",
       "      <th>y_16</th>\n",
       "      <th>y_17</th>\n",
       "      <th>y_18</th>\n",
       "      <th>y_19</th>\n",
       "      <th>y_20</th>\n",
       "      <th>y_21</th>\n",
       "      <th>y_22</th>\n",
       "      <th>y_23</th>\n",
       "      <th>y_24</th>\n",
       "      <th>y_25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "      <td>317.3</td>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.90</td>\n",
       "      <td>317.20</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.90</td>\n",
       "      <td>317.55</td>\n",
       "      <td>...</td>\n",
       "      <td>315.60</td>\n",
       "      <td>315.80</td>\n",
       "      <td>315.40</td>\n",
       "      <td>316.15</td>\n",
       "      <td>316.90</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.80</td>\n",
       "      <td>316.75</td>\n",
       "      <td>316.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.20</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.90</td>\n",
       "      <td>317.55</td>\n",
       "      <td>317.20</td>\n",
       "      <td>...</td>\n",
       "      <td>315.80</td>\n",
       "      <td>315.40</td>\n",
       "      <td>316.15</td>\n",
       "      <td>316.90</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.80</td>\n",
       "      <td>316.75</td>\n",
       "      <td>316.70</td>\n",
       "      <td>316.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.90</td>\n",
       "      <td>317.55</td>\n",
       "      <td>317.20</td>\n",
       "      <td>316.85</td>\n",
       "      <td>...</td>\n",
       "      <td>315.40</td>\n",
       "      <td>316.15</td>\n",
       "      <td>316.90</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.80</td>\n",
       "      <td>316.75</td>\n",
       "      <td>316.70</td>\n",
       "      <td>316.70</td>\n",
       "      <td>317.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.5</td>\n",
       "      <td>317.90</td>\n",
       "      <td>317.55</td>\n",
       "      <td>317.20</td>\n",
       "      <td>316.85</td>\n",
       "      <td>316.50</td>\n",
       "      <td>...</td>\n",
       "      <td>316.15</td>\n",
       "      <td>316.90</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.80</td>\n",
       "      <td>316.75</td>\n",
       "      <td>316.70</td>\n",
       "      <td>316.70</td>\n",
       "      <td>317.70</td>\n",
       "      <td>317.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "      <td>316.9</td>\n",
       "      <td>317.2</td>\n",
       "      <td>317.5</td>\n",
       "      <td>317.9</td>\n",
       "      <td>317.55</td>\n",
       "      <td>317.20</td>\n",
       "      <td>316.85</td>\n",
       "      <td>316.50</td>\n",
       "      <td>316.15</td>\n",
       "      <td>...</td>\n",
       "      <td>316.90</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.60</td>\n",
       "      <td>316.80</td>\n",
       "      <td>316.75</td>\n",
       "      <td>316.70</td>\n",
       "      <td>316.70</td>\n",
       "      <td>317.70</td>\n",
       "      <td>317.10</td>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-09</th>\n",
       "      <td>369.6</td>\n",
       "      <td>369.3</td>\n",
       "      <td>369.5</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.8</td>\n",
       "      <td>370.20</td>\n",
       "      <td>369.90</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.30</td>\n",
       "      <td>371.10</td>\n",
       "      <td>...</td>\n",
       "      <td>367.40</td>\n",
       "      <td>367.80</td>\n",
       "      <td>367.60</td>\n",
       "      <td>368.10</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.80</td>\n",
       "      <td>369.70</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-16</th>\n",
       "      <td>369.3</td>\n",
       "      <td>369.5</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.8</td>\n",
       "      <td>370.2</td>\n",
       "      <td>369.90</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.30</td>\n",
       "      <td>371.10</td>\n",
       "      <td>371.70</td>\n",
       "      <td>...</td>\n",
       "      <td>367.80</td>\n",
       "      <td>367.60</td>\n",
       "      <td>368.10</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.80</td>\n",
       "      <td>369.70</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-23</th>\n",
       "      <td>369.5</td>\n",
       "      <td>369.8</td>\n",
       "      <td>369.8</td>\n",
       "      <td>370.2</td>\n",
       "      <td>369.9</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.30</td>\n",
       "      <td>371.10</td>\n",
       "      <td>371.70</td>\n",
       "      <td>371.20</td>\n",
       "      <td>...</td>\n",
       "      <td>367.60</td>\n",
       "      <td>368.10</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.80</td>\n",
       "      <td>369.70</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-30</th>\n",
       "      <td>369.8</td>\n",
       "      <td>369.8</td>\n",
       "      <td>370.2</td>\n",
       "      <td>369.9</td>\n",
       "      <td>370.8</td>\n",
       "      <td>371.30</td>\n",
       "      <td>371.10</td>\n",
       "      <td>371.70</td>\n",
       "      <td>371.20</td>\n",
       "      <td>372.20</td>\n",
       "      <td>...</td>\n",
       "      <td>368.10</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.80</td>\n",
       "      <td>369.70</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.20</td>\n",
       "      <td>371.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-06</th>\n",
       "      <td>369.8</td>\n",
       "      <td>370.2</td>\n",
       "      <td>369.9</td>\n",
       "      <td>370.8</td>\n",
       "      <td>371.3</td>\n",
       "      <td>371.10</td>\n",
       "      <td>371.70</td>\n",
       "      <td>371.20</td>\n",
       "      <td>372.20</td>\n",
       "      <td>372.20</td>\n",
       "      <td>...</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.70</td>\n",
       "      <td>368.80</td>\n",
       "      <td>369.70</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.30</td>\n",
       "      <td>370.80</td>\n",
       "      <td>371.20</td>\n",
       "      <td>371.30</td>\n",
       "      <td>371.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2233 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2    x_1    x_2    x_3    x_4     x_5     x_6     x_7     x_8  \\\n",
       "1958-03-29  316.1  317.3  317.6  317.5  316.4  316.90  317.20  317.50  317.90   \n",
       "1958-04-05  317.3  317.6  317.5  316.4  316.9  317.20  317.50  317.90  317.55   \n",
       "1958-04-12  317.6  317.5  316.4  316.9  317.2  317.50  317.90  317.55  317.20   \n",
       "1958-04-19  317.5  316.4  316.9  317.2  317.5  317.90  317.55  317.20  316.85   \n",
       "1958-04-26  316.4  316.9  317.2  317.5  317.9  317.55  317.20  316.85  316.50   \n",
       "...           ...    ...    ...    ...    ...     ...     ...     ...     ...   \n",
       "2000-12-09  369.6  369.3  369.5  369.8  369.8  370.20  369.90  370.80  371.30   \n",
       "2000-12-16  369.3  369.5  369.8  369.8  370.2  369.90  370.80  371.30  371.10   \n",
       "2000-12-23  369.5  369.8  369.8  370.2  369.9  370.80  371.30  371.10  371.70   \n",
       "2000-12-30  369.8  369.8  370.2  369.9  370.8  371.30  371.10  371.70  371.20   \n",
       "2001-01-06  369.8  370.2  369.9  370.8  371.3  371.10  371.70  371.20  372.20   \n",
       "\n",
       "               x_9  ...    y_16    y_17    y_18    y_19    y_20    y_21  \\\n",
       "1958-03-29  317.55  ...  315.60  315.80  315.40  316.15  316.90  316.60   \n",
       "1958-04-05  317.20  ...  315.80  315.40  316.15  316.90  316.60  316.60   \n",
       "1958-04-12  316.85  ...  315.40  316.15  316.90  316.60  316.60  316.80   \n",
       "1958-04-19  316.50  ...  316.15  316.90  316.60  316.60  316.80  316.75   \n",
       "1958-04-26  316.15  ...  316.90  316.60  316.60  316.80  316.75  316.70   \n",
       "...            ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "2000-12-09  371.10  ...  367.40  367.80  367.60  368.10  368.70  368.70   \n",
       "2000-12-16  371.70  ...  367.80  367.60  368.10  368.70  368.70  368.80   \n",
       "2000-12-23  371.20  ...  367.60  368.10  368.70  368.70  368.80  369.70   \n",
       "2000-12-30  372.20  ...  368.10  368.70  368.70  368.80  369.70  370.30   \n",
       "2001-01-06  372.20  ...  368.70  368.70  368.80  369.70  370.30  370.30   \n",
       "\n",
       "              y_22    y_23    y_24   y_25  \n",
       "1958-03-29  316.60  316.80  316.75  316.7  \n",
       "1958-04-05  316.80  316.75  316.70  316.7  \n",
       "1958-04-12  316.75  316.70  316.70  317.7  \n",
       "1958-04-19  316.70  316.70  317.70  317.1  \n",
       "1958-04-26  316.70  317.70  317.10  317.6  \n",
       "...            ...     ...     ...    ...  \n",
       "2000-12-09  368.80  369.70  370.30  370.3  \n",
       "2000-12-16  369.70  370.30  370.30  370.8  \n",
       "2000-12-23  370.30  370.30  370.80  371.2  \n",
       "2000-12-30  370.30  370.80  371.20  371.3  \n",
       "2001-01-06  370.80  371.20  371.30  371.5  \n",
       "\n",
       "[2233 rows x 52 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3bd346e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cols = [col for col in seq_df.columns if col.startswith('x')]\n",
    "\n",
    "X_cols.insert(0, 'co2')\n",
    "\n",
    "y_cols = [col for col in seq_df.columns if col.startswith('y')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2c3011e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = seq_df[X_cols][:-2].values\n",
    "y_train = seq_df[y_cols][:-2].values\n",
    "\n",
    "X_test = seq_df[X_cols][-2:].values\n",
    "y_test = seq_df[y_cols][-2:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc19be68",
   "metadata": {},
   "source": [
    "### Apply a decision tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "04d134f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_seq = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "dt_seq.fit(X_train, y_train)\n",
    "\n",
    "dt_seq_preds = dt_seq.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006efe4d",
   "metadata": {},
   "source": [
    "### Apply gradient boosting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9b9082b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multioutput import RegressorChain\n",
    "\n",
    "gbr_seq = GradientBoostingRegressor(random_state=42)\n",
    "\n",
    "chained_gbr = RegressorChain(gbr_seq)\n",
    "\n",
    "chained_gbr.fit(X_train, y_train)\n",
    "\n",
    "gbr_seq_preds = chained_gbr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f615aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_dt_seq = mape(dt_seq_preds.reshape(1, -1), y_test.reshape(1, -1))\n",
    "mape_dt_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50a67337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_gbr_seq = mape(gbr_seq_preds.reshape(1, -1), y_test.reshape(1, -1))\n",
    "mape_gbr_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8914295c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_baseline = mape(X_test.reshape(1, -1), y_test.reshape(1, -1))\n",
    "mape_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00e50e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "x = ['Baseline', 'Decision Tree', 'Gradient Boosting']\n",
    "y = [mape_baseline, mape_dt_seq, mape_gbr_seq]\n",
    "\n",
    "ax.bar(x, y, width=0.4)\n",
    "ax.set_xlabel('Regressor models')\n",
    "ax.set_ylabel('MAPE (%)')\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "for index, value in enumerate(y):\n",
    "    plt.text(x=index, y=value + 0.05, s=str(value), ha='center')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "23ebd2c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dt_seq_preds[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cb84f122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 11))\n",
    "\n",
    "ax.plot(np.arange(0, 26, 1), X_test[1], 'b-', label='input')\n",
    "ax.plot(np.arange(26, 52, 1), y_test[1], marker='.', color='blue', label='Actual')\n",
    "ax.plot(np.arange(26, 52, 1), X_test[1], marker='o', color='red', label='Baseline')\n",
    "ax.plot(np.arange(26, 52, 1), dt_seq_preds[1], marker='^', color='green', label='Decision Tree')\n",
    "ax.plot(np.arange(26, 52, 1), gbr_seq_preds[1], marker='P', color='black', label='Gradient Boosting')\n",
    "\n",
    "ax.set_xlabel('Timesteps')\n",
    "ax.set_ylabel('CO2 concentration (ppmv)')\n",
    "\n",
    "plt.xticks(np.arange(1, 104, 52), np.arange(2000, 2002, 1))\n",
    "plt.legend(loc=2)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbd04d78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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