{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FJogEfYBOfUm"
   },
   "source": [
    "# Temperature Forecasting (Supplementary Notebook)\n",
    "\n",
    "Similar to what was done in previous sections, this notebook applies the methodology used in the M5 Forecasting notebook to the Jena Climate dataset. Specifically, we will be forecasting the temperature variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "olkHffRbOfUr"
   },
   "source": [
    "## 1. Jena Climate Dataset\n",
    "\n",
    "Let's load the dataset and examine it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "floWkFlxOfUs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(420551, 15)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### CREATE X,Y ####\n",
    "def create_xy(series, window_size, prediction_horizon, shuffle = False):\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(0, len(series)):\n",
    "        if len(series[(i + window_size):(i + window_size + prediction_horizon)]) < prediction_horizon:\n",
    "            break\n",
    "        x.append(series[i:(i + window_size)])\n",
    "        y.append(series[(i + window_size):(i + window_size + prediction_horizon)])\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    return x,y\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15, 5]\n",
    "\n",
    "np.set_printoptions(precision = 6, suppress = True)\n",
    "\n",
    "df = pd.read_csv('../data/jena_climate_2009_2016.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "mjsCtvwkOfUt",
    "outputId": "506c2c09-de24-4f59-956c-55fe6242e08f"
   },
   "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>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>996.57</td>\n",
       "      <td>-8.41</td>\n",
       "      <td>265.01</td>\n",
       "      <td>-9.28</td>\n",
       "      <td>93.4</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1309.80</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>136.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.51</td>\n",
       "      <td>264.91</td>\n",
       "      <td>-9.31</td>\n",
       "      <td>93.9</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1310.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.63</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.31</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>94.2</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1309.19</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>265.15</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>94.1</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1309.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>214.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01.01.2009 01:00:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01.01.2009 01:10:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-7.62</td>\n",
       "      <td>265.81</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>94.8</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1305.68</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>166.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01.01.2009 01:20:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-7.62</td>\n",
       "      <td>265.81</td>\n",
       "      <td>-8.36</td>\n",
       "      <td>94.4</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1305.69</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.50</td>\n",
       "      <td>118.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01.01.2009 01:30:00</td>\n",
       "      <td>996.50</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>265.52</td>\n",
       "      <td>-8.73</td>\n",
       "      <td>93.8</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1307.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.75</td>\n",
       "      <td>188.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01.01.2009 01:40:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.43</td>\n",
       "      <td>264.99</td>\n",
       "      <td>-9.34</td>\n",
       "      <td>93.1</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1309.85</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.88</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
       "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
       "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
       "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
       "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
       "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
       "5  01.01.2009 01:00:00    996.50     -8.05    265.38        -8.78    94.4   \n",
       "6  01.01.2009 01:10:00    996.50     -7.62    265.81        -8.30    94.8   \n",
       "7  01.01.2009 01:20:00    996.50     -7.62    265.81        -8.36    94.4   \n",
       "8  01.01.2009 01:30:00    996.50     -7.91    265.52        -8.73    93.8   \n",
       "9  01.01.2009 01:40:00    996.53     -8.43    264.99        -9.34    93.1   \n",
       "\n",
       "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
       "0          3.33          3.11          0.22       1.94             3.12   \n",
       "1          3.23          3.02          0.21       1.89             3.03   \n",
       "2          3.21          3.01          0.20       1.88             3.02   \n",
       "3          3.26          3.07          0.19       1.92             3.08   \n",
       "4          3.27          3.08          0.19       1.92             3.09   \n",
       "5          3.33          3.14          0.19       1.96             3.15   \n",
       "6          3.44          3.26          0.18       2.04             3.27   \n",
       "7          3.44          3.25          0.19       2.03             3.26   \n",
       "8          3.36          3.15          0.21       1.97             3.16   \n",
       "9          3.23          3.00          0.22       1.88             3.02   \n",
       "\n",
       "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0       1307.75      1.03           1.75     152.3  \n",
       "1       1309.80      0.72           1.50     136.1  \n",
       "2       1310.24      0.19           0.63     171.6  \n",
       "3       1309.19      0.34           0.50     198.0  \n",
       "4       1309.00      0.32           0.63     214.3  \n",
       "5       1307.86      0.21           0.63     192.7  \n",
       "6       1305.68      0.18           0.63     166.5  \n",
       "7       1305.69      0.19           0.50     118.6  \n",
       "8       1307.17      0.28           0.75     188.5  \n",
       "9       1309.85      0.59           0.88     185.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(420546,)\n"
     ]
    }
   ],
   "source": [
    "series = df['T (degC)'].iloc[5:]\n",
    "\n",
    "series.plot()\n",
    "plt.show()\n",
    "print(series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70091,)\n"
     ]
    }
   ],
   "source": [
    "series = series.iloc[::6]\n",
    "\n",
    "series.plot()\n",
    "plt.show()\n",
    "print(series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.30</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-7.62</td>\n",
       "      <td>265.81</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>94.80</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1305.68</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>166.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>996.63</td>\n",
       "      <td>-8.85</td>\n",
       "      <td>264.57</td>\n",
       "      <td>-9.70</td>\n",
       "      <td>93.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1312.11</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.50</td>\n",
       "      <td>158.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>996.87</td>\n",
       "      <td>-8.84</td>\n",
       "      <td>264.56</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>93.50</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1312.37</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.25</td>\n",
       "      <td>129.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>997.05</td>\n",
       "      <td>-9.23</td>\n",
       "      <td>264.15</td>\n",
       "      <td>-10.25</td>\n",
       "      <td>92.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1314.62</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>203.9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420522</th>\n",
       "      <td>1002.08</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>271.59</td>\n",
       "      <td>-6.10</td>\n",
       "      <td>70.20</td>\n",
       "      <td>5.51</td>\n",
       "      <td>3.87</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1282.68</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.68</td>\n",
       "      <td>207.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420528</th>\n",
       "      <td>1001.42</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>270.90</td>\n",
       "      <td>-7.08</td>\n",
       "      <td>68.77</td>\n",
       "      <td>5.21</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1285.50</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.24</td>\n",
       "      <td>184.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420534</th>\n",
       "      <td>1001.05</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>270.47</td>\n",
       "      <td>-6.97</td>\n",
       "      <td>71.80</td>\n",
       "      <td>5.04</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1287.20</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.64</td>\n",
       "      <td>129.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420540</th>\n",
       "      <td>1000.51</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>269.90</td>\n",
       "      <td>-7.63</td>\n",
       "      <td>71.40</td>\n",
       "      <td>4.81</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1.38</td>\n",
       "      <td>2.14</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1289.50</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.54</td>\n",
       "      <td>207.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420546</th>\n",
       "      <td>1000.07</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>269.10</td>\n",
       "      <td>-8.13</td>\n",
       "      <td>73.10</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1292.98</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.52</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70092 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  VPmax (mbar)  \\\n",
       "0         996.52     -8.02    265.40        -8.90   93.30          3.33   \n",
       "6         996.50     -7.62    265.81        -8.30   94.80          3.44   \n",
       "12        996.63     -8.85    264.57        -9.70   93.50          3.12   \n",
       "18        996.87     -8.84    264.56        -9.69   93.50          3.13   \n",
       "24        997.05     -9.23    264.15       -10.25   92.20          3.03   \n",
       "...          ...       ...       ...          ...     ...           ...   \n",
       "420522   1002.08     -1.40    271.59        -6.10   70.20          5.51   \n",
       "420528   1001.42     -2.15    270.90        -7.08   68.77          5.21   \n",
       "420534   1001.05     -2.61    270.47        -6.97   71.80          5.04   \n",
       "420540   1000.51     -3.22    269.90        -7.63   71.40          4.81   \n",
       "420546   1000.07     -4.05    269.10        -8.13   73.10          4.52   \n",
       "\n",
       "        VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
       "0               3.11          0.22       1.94             3.12       1307.75   \n",
       "6               3.26          0.18       2.04             3.27       1305.68   \n",
       "12              2.92          0.20       1.82             2.93       1312.11   \n",
       "18              2.92          0.20       1.83             2.93       1312.37   \n",
       "24              2.79          0.24       1.74             2.80       1314.62   \n",
       "...              ...           ...        ...              ...           ...   \n",
       "420522          3.87          1.64       2.40             3.86       1282.68   \n",
       "420528          3.59          1.63       2.23             3.58       1285.50   \n",
       "420534          3.62          1.42       2.25             3.61       1287.20   \n",
       "420540          3.44          1.38       2.14             3.44       1289.50   \n",
       "420546          3.30          1.22       2.06             3.30       1292.98   \n",
       "\n",
       "        wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0           1.03           1.75     152.3  \n",
       "6           0.18           0.63     166.5  \n",
       "12          0.16           0.50     158.3  \n",
       "18          0.07           0.25     129.3  \n",
       "24          0.10           0.38     203.9  \n",
       "...          ...            ...       ...  \n",
       "420522      1.08           1.68     207.5  \n",
       "420528      0.79           1.24     184.3  \n",
       "420534      0.77           1.64     129.1  \n",
       "420540      0.85           1.54     207.8  \n",
       "420546      0.67           1.52     240.0  \n",
       "\n",
       "[70092 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = df.iloc[::6, 1:]\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34805, 240)\n",
      "(34805,)\n",
      "(17284, 240)\n",
      "(17284,)\n",
      "(17283, 240)\n",
      "(17283,)\n"
     ]
    }
   ],
   "source": [
    "### HYPERPARAMETERS ###\n",
    "window_size = 240\n",
    "prediction_horizon = 1\n",
    "\n",
    "### TRAIN TEST VAL SPLIT ###\n",
    "train_series = series.iloc[:35045, 1]\n",
    "val_series = series.iloc[35045:52569, 1]\n",
    "test_series = series.iloc[52569:, 1]\n",
    "\n",
    "train_x, train_y = create_xy(train_series.to_numpy(), window_size, prediction_horizon)\n",
    "val_x, val_y = create_xy(val_series.to_numpy(), window_size, prediction_horizon)\n",
    "test_x, test_y = create_xy(test_series.to_numpy(), window_size, prediction_horizon)\n",
    "\n",
    "train_y = train_y.flatten()\n",
    "val_y = val_y.flatten()\n",
    "test_y = test_y.flatten()\n",
    "\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(val_x.shape)\n",
    "print(val_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "gHANelCHOfU1",
    "outputId": "78ca90b6-3427-4b2e-8b35-da0a942c9145"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_series.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4iHnJwMcOfU1"
   },
   "source": [
    "###  LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9pNItUGaOfU2",
    "outputId": "a48a88e4-41e0-4b2f-deb4-fc431a3f96b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "model = lgb.LGBMRegressor()\n",
    "\n",
    "model.fit(train_x, train_y,\n",
    "          eval_metric = 'l1', \n",
    "          eval_set = [(val_x, val_y)],\n",
    "          early_stopping_rounds = 100,\n",
    "          verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "nck0f2RyOfU5",
    "outputId": "e8f8e75d-6e4f-4481-f059-f40c67695315"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  LightGBM MAE: 0.5319\n"
     ]
    }
   ],
   "source": [
    "forecast = model.predict(test_x)\n",
    "print('  LightGBM MAE: %.4f' % (np.mean(np.abs(forecast - test_y))))\n",
    "\n",
    "#series[-test_size:].plot(marker = 'o', linestyle = '--')\n",
    "#plt.plot(forecast, marker = 'o', linestyle = '--')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34782, 240)\n",
      "(34782, 24)\n",
      "(17261, 240)\n",
      "(17261, 24)\n",
      "(17260, 240)\n",
      "(17260, 24)\n"
     ]
    }
   ],
   "source": [
    "### HYPERPARAMETERS ###\n",
    "window_size = 240\n",
    "prediction_horizon = 24\n",
    "\n",
    "### TRAIN TEST VAL SPLIT ###\n",
    "train_series = series.iloc[:35045, 1]\n",
    "val_series = series.iloc[35045:52569, 1]\n",
    "test_series = series.iloc[52569:, 1]\n",
    "\n",
    "train_x, train_y = create_xy(train_series.to_numpy(), window_size, prediction_horizon)\n",
    "val_x, val_y = create_xy(val_series.to_numpy(), window_size, prediction_horizon)\n",
    "test_x, test_y = create_xy(test_series.to_numpy(), window_size, prediction_horizon)\n",
    "\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(val_x.shape)\n",
    "print(val_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM\n",
      "MAE: 2.083323119819872\n",
      "RMSE: 2.7826767013777443\n",
      "\n",
      "Naive\n",
      "MAE: 3.184767754022055\n",
      "RMSE: 4.457663461439199\n",
      "\n",
      "Seasonal Naive\n",
      "MAE: 2.613232738911751\n",
      "RMSE: 3.4171095400347133\n"
     ]
    }
   ],
   "source": [
    "### LightGBM\n",
    "\n",
    "recursive_x = test_x\n",
    "forecast_ms = []\n",
    "for j in range(prediction_horizon):\n",
    "    pred = model.predict(recursive_x)\n",
    "    recursive_x = np.hstack((recursive_x[:, 1:], pred[:, np.newaxis]))\n",
    "    forecast_ms.append(pred)\n",
    "forecast_ms = np.asarray(forecast_ms).T\n",
    "print('LightGBM')\n",
    "print('MAE:', np.mean(np.abs(test_y - forecast_ms)))\n",
    "print('RMSE:', np.sqrt(np.mean((test_y - forecast_ms)**2)))\n",
    "print('')\n",
    "\n",
    "### Naive\n",
    "\n",
    "tmp = test_y[:-1, -1]\n",
    "n_forecast = []\n",
    "\n",
    "for val in tmp:\n",
    "    n_forecast.append(np.repeat(val, 24))\n",
    "n_forecast = np.asarray(n_forecast)\n",
    "\n",
    "print('Naive')\n",
    "print('MAE:', np.mean(np.abs(test_y[1:, :] - n_forecast)))\n",
    "print('RMSE:', np.sqrt(np.mean((test_y[1:, :] - n_forecast)**2)))\n",
    "print('')\n",
    "\n",
    "\n",
    "### S.Naive\n",
    "\n",
    "new_test = np.vstack(np.array_split(test_series.to_numpy()[3:], 730))\n",
    "\n",
    "print('Seasonal Naive')\n",
    "print('MAE:', np.mean(np.abs(new_test[1:, :] - new_test[:-1, :])))\n",
    "print('RMSE:', np.sqrt(np.mean((new_test[1:, :] - new_test[:-1, :])**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FJogEfYBOfUm"
   },
   "source": [
    "## Summary\n",
    "\n",
    "Using LightGBM with a recursive forecasting strategy demonstrates that we can achieve an MAE of about 2.08°C without hyperparameter tuning."
   ]
  }
 ],
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