weather.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATIONS_ID</th>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th>QN_9</th>\n",
       "      <th>TT_TU</th>\n",
       "      <th>RF_TU</th>\n",
       "      <th>eor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5906</td>\n",
       "      <td>1948010101</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5906</td>\n",
       "      <td>1948010102</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>89.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5906</td>\n",
       "      <td>1948010103</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5906</td>\n",
       "      <td>1948010104</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5906</td>\n",
       "      <td>1948010105</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>87.0</td>\n",
       "      <td>eor</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>666127</th>\n",
       "      <td>5906</td>\n",
       "      <td>2023123119</td>\n",
       "      <td>3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>79.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666128</th>\n",
       "      <td>5906</td>\n",
       "      <td>2023123120</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>77.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666129</th>\n",
       "      <td>5906</td>\n",
       "      <td>2023123121</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666130</th>\n",
       "      <td>5906</td>\n",
       "      <td>2023123122</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>78.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666131</th>\n",
       "      <td>5906</td>\n",
       "      <td>2023123123</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>79.0</td>\n",
       "      <td>eor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666132 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        STATIONS_ID  MESS_DATUM  QN_9  TT_TU  RF_TU  eor\n",
       "0              5906  1948010101     5   -0.3   90.0  eor\n",
       "1              5906  1948010102     5   -0.3   89.0  eor\n",
       "2              5906  1948010103     5    0.1   89.0  eor\n",
       "3              5906  1948010104     5    0.5   91.0  eor\n",
       "4              5906  1948010105     5    0.8   87.0  eor\n",
       "...             ...         ...   ...    ...    ...  ...\n",
       "666127         5906  2023123119     3    6.8   79.0  eor\n",
       "666128         5906  2023123120     3    7.2   77.0  eor\n",
       "666129         5906  2023123121     3    7.2   76.0  eor\n",
       "666130         5906  2023123122     3    6.7   78.0  eor\n",
       "666131         5906  2023123123     3    6.7   79.0  eor\n",
       "\n",
       "[666132 rows x 6 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Weather = pd.read_csv(\"dataset/produkt_tu_stunde_19480101_20231231_05906.txt\", delimiter=\";\")\n",
    "Weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{5906}\n",
      "{'eor'}\n"
     ]
    }
   ],
   "source": [
    "print(set(Weather['STATIONS_ID']))\n",
    "print(set(Weather['eor']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data was recorded in the same Mannheim weather station, thus we can remove the redundant columns. We will also drop the quality of the weather measurement (\"QN_9\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th>TT_TU</th>\n",
       "      <th>RF_TU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01 01:00:00</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-01-01 02:00:00</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-01-01 03:00:00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-01-01 04:00:00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01 05:00:00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666127</th>\n",
       "      <td>2023-12-31 19:00:00</td>\n",
       "      <td>6.8</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666128</th>\n",
       "      <td>2023-12-31 20:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666129</th>\n",
       "      <td>2023-12-31 21:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666130</th>\n",
       "      <td>2023-12-31 22:00:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666131</th>\n",
       "      <td>2023-12-31 23:00:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666132 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MESS_DATUM  TT_TU  RF_TU\n",
       "0      1948-01-01 01:00:00   -0.3   90.0\n",
       "1      1948-01-01 02:00:00   -0.3   89.0\n",
       "2      1948-01-01 03:00:00    0.1   89.0\n",
       "3      1948-01-01 04:00:00    0.5   91.0\n",
       "4      1948-01-01 05:00:00    0.8   87.0\n",
       "...                    ...    ...    ...\n",
       "666127 2023-12-31 19:00:00    6.8   79.0\n",
       "666128 2023-12-31 20:00:00    7.2   77.0\n",
       "666129 2023-12-31 21:00:00    7.2   76.0\n",
       "666130 2023-12-31 22:00:00    6.7   78.0\n",
       "666131 2023-12-31 23:00:00    6.7   79.0\n",
       "\n",
       "[666132 rows x 3 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Weather = Weather.drop(columns=['STATIONS_ID', 'eor', 'QN_9'])\n",
    "\n",
    "Weather['MESS_DATUM'] = pd.to_datetime(Weather['MESS_DATUM'], format='%Y%m%d%H')  # conver to proper time format\n",
    "\n",
    "Weather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In those 76 years of measurement some a few dozen entries are inevitable missing due to error. We will interpolate them using the mean of the surrounding entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MESS_DATUM</th>\n",
       "      <th>TT_TU</th>\n",
       "      <th>RF_TU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01 01:00:00</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-01-01 02:00:00</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-01-01 03:00:00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-01-01 04:00:00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01 05:00:00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666210</th>\n",
       "      <td>2023-12-31 19:00:00</td>\n",
       "      <td>6.8</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666211</th>\n",
       "      <td>2023-12-31 20:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666212</th>\n",
       "      <td>2023-12-31 21:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666213</th>\n",
       "      <td>2023-12-31 22:00:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666214</th>\n",
       "      <td>2023-12-31 23:00:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666215 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MESS_DATUM  TT_TU  RF_TU\n",
       "0      1948-01-01 01:00:00   -0.3   90.0\n",
       "1      1948-01-01 02:00:00   -0.3   89.0\n",
       "2      1948-01-01 03:00:00    0.1   89.0\n",
       "3      1948-01-01 04:00:00    0.5   91.0\n",
       "4      1948-01-01 05:00:00    0.8   87.0\n",
       "...                    ...    ...    ...\n",
       "666210 2023-12-31 19:00:00    6.8   79.0\n",
       "666211 2023-12-31 20:00:00    7.2   77.0\n",
       "666212 2023-12-31 21:00:00    7.2   76.0\n",
       "666213 2023-12-31 22:00:00    6.7   78.0\n",
       "666214 2023-12-31 23:00:00    6.7   79.0\n",
       "\n",
       "[666215 rows x 3 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Weather = Weather.set_index('MESS_DATUM')\n",
    "\n",
    "# Resample the DataFrame to hourly frequency to identify missing timestamps\n",
    "Weather = Weather.resample('h').asfreq()\n",
    "\n",
    "Weather['TT_TU'] = Weather['TT_TU'].interpolate(method='linear')\n",
    "Weather['RF_TU'] = Weather['RF_TU'].interpolate(method='linear')\n",
    "\n",
    "Weather = Weather.reset_index()\n",
    "Weather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a preprocessed time series of air temperature in degree Celsius (\"TT_TU\") and relative humidity as a percentage (\"RF_TU\")."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Regression Models\n",
    "\n",
    "We will first try simple regression models to predict air temperature and humidity in 6 hours based on the current time and measurments of the last 10 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TT_TU_lag_10</th>\n",
       "      <th>TT_TU_lag_9</th>\n",
       "      <th>TT_TU_lag_8</th>\n",
       "      <th>TT_TU_lag_7</th>\n",
       "      <th>TT_TU_lag_6</th>\n",
       "      <th>TT_TU_lag_5</th>\n",
       "      <th>TT_TU_lag_4</th>\n",
       "      <th>TT_TU_lag_3</th>\n",
       "      <th>TT_TU_lag_2</th>\n",
       "      <th>TT_TU_lag_1</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666195</th>\n",
       "      <td>5.7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>14</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666196</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>15</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666197</th>\n",
       "      <td>6.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666198</th>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>17</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666199</th>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>18</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666200 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TT_TU_lag_10  TT_TU_lag_9  TT_TU_lag_8  TT_TU_lag_7  TT_TU_lag_6  \\\n",
       "0               -0.3         -0.3          0.1          0.5          0.8   \n",
       "1               -0.3          0.1          0.5          0.8          0.8   \n",
       "2                0.1          0.5          0.8          0.8          0.7   \n",
       "3                0.5          0.8          0.8          0.7          0.6   \n",
       "4                0.8          0.8          0.7          0.6          1.1   \n",
       "...              ...          ...          ...          ...          ...   \n",
       "666195           5.7          5.5          6.5          7.6          7.2   \n",
       "666196           5.5          6.5          7.6          7.2          7.9   \n",
       "666197           6.5          7.6          7.2          7.9          8.4   \n",
       "666198           7.6          7.2          7.9          8.4          8.7   \n",
       "666199           7.2          7.9          8.4          8.7          8.4   \n",
       "\n",
       "        TT_TU_lag_5  TT_TU_lag_4  TT_TU_lag_3  TT_TU_lag_2  TT_TU_lag_1  Hour  \\\n",
       "0               0.8          0.7          0.6          1.1          1.0    11   \n",
       "1               0.7          0.6          1.1          1.0          1.1    12   \n",
       "2               0.6          1.1          1.0          1.1          1.1    13   \n",
       "3               1.1          1.0          1.1          1.1          1.1    14   \n",
       "4               1.0          1.1          1.1          1.1          1.1    15   \n",
       "...             ...          ...          ...          ...          ...   ...   \n",
       "666195          7.9          8.4          8.7          8.4          8.8    14   \n",
       "666196          8.4          8.7          8.4          8.8          8.9    15   \n",
       "666197          8.7          8.4          8.8          8.9          8.2    16   \n",
       "666198          8.4          8.8          8.9          8.2          7.9    17   \n",
       "666199          8.8          8.9          8.2          7.9          7.3    18   \n",
       "\n",
       "        Days  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "666195   365  \n",
       "666196   365  \n",
       "666197   365  \n",
       "666198   365  \n",
       "666199   365  \n",
       "\n",
       "[666200 rows x 12 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add last 10 entries to regression matrix X\n",
    "lags = 10\n",
    "forecast_shift = 5\n",
    "X_a = np.zeros((len(Weather) - lags - forecast_shift, lags))\n",
    "y_a = np.zeros(len(Weather) - lags - forecast_shift)\n",
    "\n",
    "for i in range(lags, len(Weather) - forecast_shift):\n",
    "    X_a[i - lags] = Weather['TT_TU'].iloc[i - lags:i].values\n",
    "    y_a[i - lags] = Weather['TT_TU'].iloc[i + forecast_shift]\n",
    "\n",
    "X = pd.DataFrame(X_a, columns=[f'TT_TU_lag_{i}' for i in range(lags, 0, -1)])\n",
    "y = pd.Series(y_a, name='TT_TU')\n",
    "\n",
    "X['Hour'] = Weather['MESS_DATUM'].iloc[lags:-forecast_shift].dt.hour.values\n",
    "X['Days'] = Weather['MESS_DATUM'].iloc[lags:-forecast_shift].dt.dayofyear.values\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the training we scale all columns with the standard scaler of `sklearn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>TT_TU_lag_10</th>\n",
       "      <th>TT_TU_lag_9</th>\n",
       "      <th>TT_TU_lag_8</th>\n",
       "      <th>TT_TU_lag_7</th>\n",
       "      <th>TT_TU_lag_6</th>\n",
       "      <th>TT_TU_lag_5</th>\n",
       "      <th>TT_TU_lag_4</th>\n",
       "      <th>TT_TU_lag_3</th>\n",
       "      <th>TT_TU_lag_2</th>\n",
       "      <th>TT_TU_lag_1</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.581240</td>\n",
       "      <td>-0.581241</td>\n",
       "      <td>-0.559593</td>\n",
       "      <td>-0.537944</td>\n",
       "      <td>-0.521708</td>\n",
       "      <td>-0.521709</td>\n",
       "      <td>-0.527122</td>\n",
       "      <td>-0.532535</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>-0.510887</td>\n",
       "      <td>-0.072237</td>\n",
       "      <td>-1.727367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.581240</td>\n",
       "      <td>-0.559592</td>\n",
       "      <td>-0.537944</td>\n",
       "      <td>-0.521708</td>\n",
       "      <td>-0.521708</td>\n",
       "      <td>-0.527121</td>\n",
       "      <td>-0.532534</td>\n",
       "      <td>-0.505474</td>\n",
       "      <td>-0.510887</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>0.072227</td>\n",
       "      <td>-1.727367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.559591</td>\n",
       "      <td>-0.537943</td>\n",
       "      <td>-0.521707</td>\n",
       "      <td>-0.521708</td>\n",
       "      <td>-0.527121</td>\n",
       "      <td>-0.532534</td>\n",
       "      <td>-0.505473</td>\n",
       "      <td>-0.510886</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>0.216690</td>\n",
       "      <td>-1.727367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.537942</td>\n",
       "      <td>-0.521706</td>\n",
       "      <td>-0.521707</td>\n",
       "      <td>-0.527120</td>\n",
       "      <td>-0.532533</td>\n",
       "      <td>-0.505472</td>\n",
       "      <td>-0.510885</td>\n",
       "      <td>-0.505474</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>0.361154</td>\n",
       "      <td>-1.727367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.521705</td>\n",
       "      <td>-0.521706</td>\n",
       "      <td>-0.527119</td>\n",
       "      <td>-0.532532</td>\n",
       "      <td>-0.505472</td>\n",
       "      <td>-0.510885</td>\n",
       "      <td>-0.505473</td>\n",
       "      <td>-0.505474</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>-0.505475</td>\n",
       "      <td>0.505618</td>\n",
       "      <td>-1.727367</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666195</th>\n",
       "      <td>-0.256505</td>\n",
       "      <td>-0.267330</td>\n",
       "      <td>-0.213209</td>\n",
       "      <td>-0.153675</td>\n",
       "      <td>-0.175324</td>\n",
       "      <td>-0.137439</td>\n",
       "      <td>-0.110379</td>\n",
       "      <td>-0.094142</td>\n",
       "      <td>-0.110380</td>\n",
       "      <td>-0.088731</td>\n",
       "      <td>0.361154</td>\n",
       "      <td>1.724960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666196</th>\n",
       "      <td>-0.267330</td>\n",
       "      <td>-0.213208</td>\n",
       "      <td>-0.153674</td>\n",
       "      <td>-0.175324</td>\n",
       "      <td>-0.137438</td>\n",
       "      <td>-0.110378</td>\n",
       "      <td>-0.094142</td>\n",
       "      <td>-0.110379</td>\n",
       "      <td>-0.088731</td>\n",
       "      <td>-0.083319</td>\n",
       "      <td>0.505618</td>\n",
       "      <td>1.724960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666197</th>\n",
       "      <td>-0.213207</td>\n",
       "      <td>-0.153673</td>\n",
       "      <td>-0.175323</td>\n",
       "      <td>-0.137438</td>\n",
       "      <td>-0.110377</td>\n",
       "      <td>-0.094141</td>\n",
       "      <td>-0.110379</td>\n",
       "      <td>-0.088730</td>\n",
       "      <td>-0.083318</td>\n",
       "      <td>-0.121205</td>\n",
       "      <td>0.650081</td>\n",
       "      <td>1.724960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666198</th>\n",
       "      <td>-0.153672</td>\n",
       "      <td>-0.175322</td>\n",
       "      <td>-0.137437</td>\n",
       "      <td>-0.110376</td>\n",
       "      <td>-0.094140</td>\n",
       "      <td>-0.110378</td>\n",
       "      <td>-0.088729</td>\n",
       "      <td>-0.083318</td>\n",
       "      <td>-0.121204</td>\n",
       "      <td>-0.137442</td>\n",
       "      <td>0.794545</td>\n",
       "      <td>1.724960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666199</th>\n",
       "      <td>-0.175321</td>\n",
       "      <td>-0.137436</td>\n",
       "      <td>-0.110376</td>\n",
       "      <td>-0.094140</td>\n",
       "      <td>-0.110377</td>\n",
       "      <td>-0.088729</td>\n",
       "      <td>-0.083317</td>\n",
       "      <td>-0.121204</td>\n",
       "      <td>-0.137441</td>\n",
       "      <td>-0.169915</td>\n",
       "      <td>0.939008</td>\n",
       "      <td>1.724960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666200 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TT_TU_lag_10  TT_TU_lag_9  TT_TU_lag_8  TT_TU_lag_7  TT_TU_lag_6  \\\n",
       "0          -0.581240    -0.581241    -0.559593    -0.537944    -0.521708   \n",
       "1          -0.581240    -0.559592    -0.537944    -0.521708    -0.521708   \n",
       "2          -0.559591    -0.537943    -0.521707    -0.521708    -0.527121   \n",
       "3          -0.537942    -0.521706    -0.521707    -0.527120    -0.532533   \n",
       "4          -0.521705    -0.521706    -0.527119    -0.532532    -0.505472   \n",
       "...              ...          ...          ...          ...          ...   \n",
       "666195     -0.256505    -0.267330    -0.213209    -0.153675    -0.175324   \n",
       "666196     -0.267330    -0.213208    -0.153674    -0.175324    -0.137438   \n",
       "666197     -0.213207    -0.153673    -0.175323    -0.137438    -0.110377   \n",
       "666198     -0.153672    -0.175322    -0.137437    -0.110376    -0.094140   \n",
       "666199     -0.175321    -0.137436    -0.110376    -0.094140    -0.110377   \n",
       "\n",
       "        TT_TU_lag_5  TT_TU_lag_4  TT_TU_lag_3  TT_TU_lag_2  TT_TU_lag_1  \\\n",
       "0         -0.521709    -0.527122    -0.532535    -0.505475    -0.510887   \n",
       "1         -0.527121    -0.532534    -0.505474    -0.510887    -0.505475   \n",
       "2         -0.532534    -0.505473    -0.510886    -0.505475    -0.505475   \n",
       "3         -0.505472    -0.510885    -0.505474    -0.505475    -0.505475   \n",
       "4         -0.510885    -0.505473    -0.505474    -0.505475    -0.505475   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "666195    -0.137439    -0.110379    -0.094142    -0.110380    -0.088731   \n",
       "666196    -0.110378    -0.094142    -0.110379    -0.088731    -0.083319   \n",
       "666197    -0.094141    -0.110379    -0.088730    -0.083318    -0.121205   \n",
       "666198    -0.110378    -0.088729    -0.083318    -0.121204    -0.137442   \n",
       "666199    -0.088729    -0.083317    -0.121204    -0.137441    -0.169915   \n",
       "\n",
       "            Hour      Days  \n",
       "0      -0.072237 -1.727367  \n",
       "1       0.072227 -1.727367  \n",
       "2       0.216690 -1.727367  \n",
       "3       0.361154 -1.727367  \n",
       "4       0.505618 -1.727367  \n",
       "...          ...       ...  \n",
       "666195  0.361154  1.724960  \n",
       "666196  0.505618  1.724960  \n",
       "666197  0.650081  1.724960  \n",
       "666198  0.794545  1.724960  \n",
       "666199  0.939008  1.724960  \n",
       "\n",
       "[666200 rows x 12 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "# scale y after conversion to nx1 matrix\n",
    "y_scaled = scaler.fit_transform(y.values.reshape(-1, 1))\n",
    "y_scaled = pd.Series(y_scaled.flatten(), name=y.name)\n",
    "\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>TT_TU</td>      <th>  R-squared:         </th>  <td>   0.325</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.325</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.141e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 29 Jun 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:29:46</td>     <th>  Log-Likelihood:    </th> <td>-6.6681e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>532960</td>      <th>  AIC:               </th>  <td>1.334e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>532947</td>      <th>  BIC:               </th>  <td>1.334e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>   -0.0010</td> <td>    0.001</td> <td>   -0.889</td> <td> 0.374</td> <td>   -0.003</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_10</th> <td>    0.0829</td> <td>    0.003</td> <td>   28.091</td> <td> 0.000</td> <td>    0.077</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_9</th>  <td>    0.0601</td> <td>    0.004</td> <td>   14.095</td> <td> 0.000</td> <td>    0.052</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_8</th>  <td>    0.0055</td> <td>    0.004</td> <td>    1.283</td> <td> 0.200</td> <td>   -0.003</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_7</th>  <td>   -0.0134</td> <td>    0.004</td> <td>   -3.206</td> <td> 0.001</td> <td>   -0.022</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_6</th>  <td>   -0.0712</td> <td>    0.004</td> <td>  -16.486</td> <td> 0.000</td> <td>   -0.080</td> <td>   -0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_5</th>  <td>    0.0346</td> <td>    0.004</td> <td>    7.937</td> <td> 0.000</td> <td>    0.026</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_4</th>  <td>    0.0989</td> <td>    0.004</td> <td>   22.924</td> <td> 0.000</td> <td>    0.090</td> <td>    0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_3</th>  <td>   -0.1337</td> <td>    0.004</td> <td>  -30.338</td> <td> 0.000</td> <td>   -0.142</td> <td>   -0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_2</th>  <td>    0.0247</td> <td>    0.004</td> <td>    5.783</td> <td> 0.000</td> <td>    0.016</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TT_TU_lag_1</th>  <td>    0.5409</td> <td>    0.003</td> <td>  185.800</td> <td> 0.000</td> <td>    0.535</td> <td>    0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Hour</th>         <td>   -0.0680</td> <td>    0.001</td> <td>  -58.108</td> <td> 0.000</td> <td>   -0.070</td> <td>   -0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Days</th>         <td>    0.0252</td> <td>    0.001</td> <td>   21.657</td> <td> 0.000</td> <td>    0.023</td> <td>    0.027</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1469264.696</td> <th>  Durbin-Watson:     </th>     <td>   2.000</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>175165583923.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>-34.586</td>   <th>  Prob(JB):          </th>     <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>2810.702</td>   <th>  Cond. No.          </th>     <td>    14.8</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      TT\\_TU      & \\textbf{  R-squared:         } &        0.325      \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &        0.325      \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &    2.141e+04      \\\\\n",
       "\\textbf{Date:}             & Sat, 29 Jun 2024 & \\textbf{  Prob (F-statistic):} &        0.00       \\\\\n",
       "\\textbf{Time:}             &     11:29:46     & \\textbf{  Log-Likelihood:    } &   -6.6681e+05     \\\\\n",
       "\\textbf{No. Observations:} &      532960      & \\textbf{  AIC:               } &    1.334e+06      \\\\\n",
       "\\textbf{Df Residuals:}     &      532947      & \\textbf{  BIC:               } &    1.334e+06      \\\\\n",
       "\\textbf{Df Model:}         &          12      & \\textbf{                     } &                   \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &                   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                         & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}           &      -0.0010  &        0.001     &    -0.889  &         0.374        &       -0.003    &        0.001     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_10} &       0.0829  &        0.003     &    28.091  &         0.000        &        0.077    &        0.089     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_9}  &       0.0601  &        0.004     &    14.095  &         0.000        &        0.052    &        0.068     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_8}  &       0.0055  &        0.004     &     1.283  &         0.200        &       -0.003    &        0.014     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_7}  &      -0.0134  &        0.004     &    -3.206  &         0.001        &       -0.022    &       -0.005     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_6}  &      -0.0712  &        0.004     &   -16.486  &         0.000        &       -0.080    &       -0.063     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_5}  &       0.0346  &        0.004     &     7.937  &         0.000        &        0.026    &        0.043     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_4}  &       0.0989  &        0.004     &    22.924  &         0.000        &        0.090    &        0.107     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_3}  &      -0.1337  &        0.004     &   -30.338  &         0.000        &       -0.142    &       -0.125     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_2}  &       0.0247  &        0.004     &     5.783  &         0.000        &        0.016    &        0.033     \\\\\n",
       "\\textbf{TT\\_TU\\_lag\\_1}  &       0.5409  &        0.003     &   185.800  &         0.000        &        0.535    &        0.547     \\\\\n",
       "\\textbf{Hour}            &      -0.0680  &        0.001     &   -58.108  &         0.000        &       -0.070    &       -0.066     \\\\\n",
       "\\textbf{Days}            &       0.0252  &        0.001     &    21.657  &         0.000        &        0.023    &        0.027     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1469264.696 & \\textbf{  Durbin-Watson:     } &        2.000      \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 175165583923.135  \\\\\n",
       "\\textbf{Skew:}          &   -34.586   & \\textbf{  Prob(JB):          } &         0.00      \\\\\n",
       "\\textbf{Kurtosis:}      &   2810.702  & \\textbf{  Cond. No.          } &         14.8      \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  TT_TU   R-squared:                       0.325\n",
       "Model:                            OLS   Adj. R-squared:                  0.325\n",
       "Method:                 Least Squares   F-statistic:                 2.141e+04\n",
       "Date:                Sat, 29 Jun 2024   Prob (F-statistic):               0.00\n",
       "Time:                        11:29:46   Log-Likelihood:            -6.6681e+05\n",
       "No. Observations:              532960   AIC:                         1.334e+06\n",
       "Df Residuals:                  532947   BIC:                         1.334e+06\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           -0.0010      0.001     -0.889      0.374      -0.003       0.001\n",
       "TT_TU_lag_10     0.0829      0.003     28.091      0.000       0.077       0.089\n",
       "TT_TU_lag_9      0.0601      0.004     14.095      0.000       0.052       0.068\n",
       "TT_TU_lag_8      0.0055      0.004      1.283      0.200      -0.003       0.014\n",
       "TT_TU_lag_7     -0.0134      0.004     -3.206      0.001      -0.022      -0.005\n",
       "TT_TU_lag_6     -0.0712      0.004    -16.486      0.000      -0.080      -0.063\n",
       "TT_TU_lag_5      0.0346      0.004      7.937      0.000       0.026       0.043\n",
       "TT_TU_lag_4      0.0989      0.004     22.924      0.000       0.090       0.107\n",
       "TT_TU_lag_3     -0.1337      0.004    -30.338      0.000      -0.142      -0.125\n",
       "TT_TU_lag_2      0.0247      0.004      5.783      0.000       0.016       0.033\n",
       "TT_TU_lag_1      0.5409      0.003    185.800      0.000       0.535       0.547\n",
       "Hour            -0.0680      0.001    -58.108      0.000      -0.070      -0.066\n",
       "Days             0.0252      0.001     21.657      0.000       0.023       0.027\n",
       "==============================================================================\n",
       "Omnibus:                  1469264.696   Durbin-Watson:                   2.000\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):     175165583923.135\n",
       "Skew:                         -34.586   Prob(JB):                         0.00\n",
       "Kurtosis:                    2810.702   Cond. No.                         14.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(y_train, sm.add_constant(X_train)).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 0.42226634030647087\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(sm.add_constant(X_test))\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error (MSE): {mse}')\n",
    "\n",
    "# Plot random date range\n",
    "size = 200\n",
    "np.random.seed(42)\n",
    "start_index = np.random.randint(0, len(X_scaled) - size)\n",
    "random_indices = np.arange(start_index, start_index + size)\n",
    "X_random = X_scaled.iloc[random_indices]\n",
    "y_random = y_scaled.iloc[random_indices - forecast_shift]\n",
    "y_pred_random = model.predict(sm.add_constant(X_random))\n",
    "\n",
    "# Plot the comparison between predicted and actual values\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(random_indices, y_random.values, label='Actual', color='blue')\n",
    "plt.plot(random_indices, y_pred_random.values, label='Predicted', color='orange')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs Predicted Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We clearly see that the linear regression model fails to make an accurate 6-hour prediction for the temperature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.3944250678613882\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Plot random date range\n",
    "size = 200\n",
    "np.random.seed(42)\n",
    "start_index = np.random.randint(0, len(X_scaled) - size)\n",
    "random_indices = np.arange(start_index, start_index + size)\n",
    "X_random = X_scaled.iloc[random_indices]\n",
    "y_random = y_scaled.iloc[random_indices - forecast_shift]\n",
    "y_pred_random = knn.predict(X_random)\n",
    "\n",
    "# Plot the comparison between predicted and actual values\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(random_indices, y_random.values, label='Actual', color='blue')\n",
    "plt.plot(random_indices, y_pred_random, label='Predicted', color='orange')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs Predicted Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k-Means does a better job, providing a somewhat usable prediction for 6 hours in advance. Does including the humidity information help our prediction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add last 10 entries to regression matrix X\n",
    "lags = 10\n",
    "forecast_shift = 5\n",
    "X_b = np.zeros((len(Weather) - lags - forecast_shift, lags))\n",
    "\n",
    "for i in range(lags, len(Weather) - forecast_shift):\n",
    "    X_b[i - lags] = Weather['RF_TU'].iloc[i - lags:i].values\n",
    "\n",
    "X_b = scaler.fit_transform(X_b)\n",
    "\n",
    "X_scaled_ext = pd.concat([X_scaled, pd.DataFrame(X_b, columns=[f'RF_TU_lag{i}' for i in range(lags, 0, -1)])], axis=1)\n",
    "\n",
    "X_train_ext, X_test_ext, y_train_ext, y_test_ext = train_test_split(X_scaled_ext, y_scaled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.40613999023168385\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=5)\n",
    "knn.fit(X_train_ext, y_train_ext)\n",
    "\n",
    "y_pred_ext = knn.predict(X_test_ext)\n",
    "\n",
    "mse = mean_squared_error(y_test_ext, y_pred_ext)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Plot random date range\n",
    "size = 200\n",
    "np.random.seed(42)\n",
    "start_index = np.random.randint(0, len(X_scaled_ext) - size)\n",
    "random_indices = np.arange(start_index, start_index + size)\n",
    "X_random = X_scaled_ext.iloc[random_indices]\n",
    "y_random = y_scaled.iloc[random_indices - forecast_shift]\n",
    "y_pred_random = knn.predict(X_random)\n",
    "\n",
    "# Plot the comparison between predicted and actual values\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(random_indices, y_random.values, label='Actual', color='blue')\n",
    "plt.plot(random_indices, y_pred_random, label='Predicted', color='orange')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs Predicted Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding humidity information does not seem to improve the temperature prediciton."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, GRU, Dense, Dropout, BatchNormalization, LSTM\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the Neural Network we need to reshape X to a 3d tensor of form (samples, time steps, features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesteps = 10  # as before\n",
    "\n",
    "# remove the feature 'Hour', since it is only provided once instead of every hour\n",
    "original_features = X_train.shape[1] - 2\n",
    "additional_features = 1\n",
    "total_features = original_features + additional_features\n",
    "\n",
    "# transform to 3d and add 'Day' data to each time stamp\n",
    "def include_days_feature(X, timesteps, feature_index):\n",
    "    expanded_X = np.zeros((X.shape[0], timesteps, total_features))\n",
    "    for i in range(timesteps):\n",
    "        expanded_X[:, i, :original_features] = X.iloc[:, :original_features].values\n",
    "        expanded_X[:, i, original_features:] = X.iloc[:, feature_index].values.reshape(-1, 1)\n",
    "    return expanded_X\n",
    "\n",
    "# Reshape data to 3D for GRU model: (samples, timesteps, features)\n",
    "X_train_reshaped = include_days_feature(X_train, timesteps, -1)\n",
    "X_test_reshaped = include_days_feature(X_test, timesteps, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we build our Recurrent Neural Network using the Gated Recurrent Unit (GRU) architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ gru_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,450</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ gru_2 (\u001b[38;5;33mGRU\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │         \u001b[38;5;34m9,450\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,501</span> (37.11 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,501\u001b[0m (37.11 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,501</span> (37.11 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,501\u001b[0m (37.11 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_final, X_val, y_train_final, y_val = train_test_split(X_train_reshaped, y_train, test_size=0.1, random_state=42)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(timesteps, total_features)))\n",
    "model.add(GRU(units=50, return_sequences=False))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.8284 - val_loss: 0.5643\n",
      "Epoch 2/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2ms/step - loss: 0.6901 - val_loss: 0.5579\n",
      "Epoch 3/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2ms/step - loss: 0.8602 - val_loss: 0.5566\n",
      "Epoch 4/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.6857 - val_loss: 0.5899\n",
      "Epoch 5/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.7216 - val_loss: 0.5762\n",
      "Epoch 6/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 2ms/step - loss: 0.6496 - val_loss: 0.5721\n",
      "Epoch 7/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 2ms/step - loss: 0.6681 - val_loss: 0.6019\n",
      "Epoch 8/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.5844 - val_loss: 0.6061\n",
      "Epoch 9/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.5681 - val_loss: 0.5771\n",
      "Epoch 10/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.6336 - val_loss: 0.5703\n",
      "Epoch 11/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.5968 - val_loss: 0.5695\n",
      "Epoch 12/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2ms/step - loss: 0.6248 - val_loss: 0.5832\n",
      "Epoch 13/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 3ms/step - loss: 0.7498 - val_loss: 0.5886\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),\n",
    "    tf.keras.callbacks.ModelCheckpoint('weather-simple.keras', save_best_only=True)\n",
    "]\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "\n",
    "history = model.fit(X_train_final, y_train_final, epochs=20, batch_size=32, validation_data=(X_val, y_val), callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4164/4164\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 871us/step\n",
      "Mean Squared Error: 0.3752163818306391\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 983us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_reshaped)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Plot the comparison between predicted and actual values for a random sample\n",
    "size = 200\n",
    "np.random.seed(42)\n",
    "start_index = np.random.randint(0, len(X_scaled) - size)\n",
    "random_indices = np.arange(start_index, start_index + size)\n",
    "\n",
    "X_random = X_scaled.iloc[random_indices]\n",
    "y_random = y_scaled.iloc[random_indices - forecast_shift]\n",
    "\n",
    "# Reshape X_random to 3D for GRU model: (samples, timesteps, features)\n",
    "X_random_reshaped = include_days_feature(X_random, timesteps, -1)\n",
    "y_pred_random = model.predict(X_random_reshaped)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(random_indices, y_random.values, label='Actual', color='blue')\n",
    "plt.plot(random_indices, y_pred_random.flatten(), label='Predicted', color='orange')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs Predicted Temperature')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So even our very small neural network with less than $10.000$ parameters outperforms our previous regression models. We will now try a slightly more complex model, which has around $32.000$ parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_8\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_8\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m50\u001b[0m)         │        \u001b[38;5;34m12,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">32,651</span> (127.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,651\u001b[0m (127.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">32,651</span> (127.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m32,651\u001b[0m (127.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Input(shape=(timesteps, total_features)))\n",
    "model.add(LSTM(units=50, return_sequences=True))  # First LSTM layer, return sequences for the next LSTM layer\n",
    "model.add(LSTM(units=50, return_sequences=False))  # Second LSTM layer\n",
    "model.add(Dense(1))  # Output layer\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 4ms/step - loss: 0.7414 - val_loss: 0.5748\n",
      "Epoch 2/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 4ms/step - loss: 0.8152 - val_loss: 0.6019\n",
      "Epoch 3/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7242 - val_loss: 0.6218\n",
      "Epoch 4/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7041 - val_loss: 0.5647\n",
      "Epoch 5/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7229 - val_loss: 0.5656\n",
      "Epoch 6/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7022 - val_loss: 0.5733\n",
      "Epoch 7/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7085 - val_loss: 0.5909\n",
      "Epoch 8/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6400 - val_loss: 0.5810\n",
      "Epoch 9/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6542 - val_loss: 0.5459\n",
      "Epoch 10/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6188 - val_loss: 0.5678\n",
      "Epoch 11/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.5771 - val_loss: 0.5662\n",
      "Epoch 12/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6956 - val_loss: 0.5808\n",
      "Epoch 13/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6813 - val_loss: 0.5781\n",
      "Epoch 14/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6850 - val_loss: 0.6022\n",
      "Epoch 15/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.6663 - val_loss: 0.5930\n",
      "Epoch 16/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 4ms/step - loss: 0.7588 - val_loss: 0.6146\n",
      "Epoch 17/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 4ms/step - loss: 0.8345 - val_loss: 0.5987\n",
      "Epoch 18/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 4ms/step - loss: 0.6755 - val_loss: 0.5779\n",
      "Epoch 19/20\n",
      "\u001b[1m14990/14990\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 4ms/step - loss: 0.6489 - val_loss: 0.5816\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),\n",
    "    tf.keras.callbacks.ModelCheckpoint('weather.keras', save_best_only=True)\n",
    "]\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "\n",
    "history = model.fit(X_train_final, y_train_final, epochs=20, batch_size=32, validation_data=(X_val, y_val), callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4164/4164\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 1ms/step\n",
      "Mean Squared Error: 0.37646910131921313\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_reshaped)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "\n",
    "# Plot the comparison between predicted and actual values for a random sample\n",
    "size = 200\n",
    "np.random.seed(42)\n",
    "start_index = np.random.randint(0, len(X_scaled) - size)\n",
    "random_indices = np.arange(start_index, start_index + size)\n",
    "\n",
    "X_random = X_scaled.iloc[random_indices]\n",
    "y_random = y_scaled.iloc[random_indices - forecast_shift]\n",
    "\n",
    "# Reshape X_random to 3D for GRU model: (samples, timesteps, features)\n",
    "X_random_reshaped = include_days_feature(X_random, timesteps, -1)\n",
    "y_pred_random = model.predict(X_random_reshaped)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(random_indices, y_random.values, label='Actual', color='blue')\n",
    "plt.plot(random_indices, y_pred_random.flatten(), label='Predicted', color='orange')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Actual vs Predicted Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at our standard example, the model seems to do a slightly better job. Although in general the mean square error did not decrease significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice, weather predictions are done using far more complicated models taking dozens of different measurement parameters in account. But we could show that a temperature prediction 6 hours ahead is possible using only temperature measurements. We could also demonstrate that even very small Recurrent neural networks outperform classical regression methods at this task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Mia Müßig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}