LSTM_Time_Series_Forecasting_of_Temperature.ipynb

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        "<a href=\"https://colab.research.google.com/github/ErikLarssonDev/Time-Series/blob/main/LSTM_Time_Series_Forecasting_of_Temperature.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# LSTM Time Series Forecasting of Temperature"
      ],
      "metadata": {
        "id": "3saVE-Hhw3Nd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing the data"
      ],
      "metadata": {
        "id": "_pleUR418OYP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "w6UpkIv3vkhj"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zip_path = tf.keras.utils.get_file(\n",
        "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
        "    fname='jena_climate_2009_2016.csv.zip',\n",
        "    extract=True)\n",
        "csv_path, _ = os.path.splitext(zip_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9931Eehv2mR",
        "outputId": "8639a62d-90cc-4be2-dce0-6dc0be72060c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip\n",
            "13568290/13568290 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(csv_path)\n",
        "df"
      ],
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          "height": 615
        },
        "id": "WC7kPstAwDOj",
        "outputId": "e0baa464-3794-471e-840e-776ce1c45e1e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  \\\n",
              "0       01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90   \n",
              "1       01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28   \n",
              "2       01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31   \n",
              "3       01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07   \n",
              "4       01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04   \n",
              "...                     ...       ...       ...       ...          ...   \n",
              "420546  31.12.2016 23:20:00   1000.07     -4.05    269.10        -8.13   \n",
              "420547  31.12.2016 23:30:00    999.93     -3.35    269.81        -8.06   \n",
              "420548  31.12.2016 23:40:00    999.82     -3.16    270.01        -8.21   \n",
              "420549  31.12.2016 23:50:00    999.81     -4.23    268.94        -8.53   \n",
              "420550  01.01.2017 00:00:00    999.82     -4.82    268.36        -8.42   \n",
              "\n",
              "        rh (%)  VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  \\\n",
              "0        93.30          3.33          3.11          0.22       1.94   \n",
              "1        93.40          3.23          3.02          0.21       1.89   \n",
              "2        93.90          3.21          3.01          0.20       1.88   \n",
              "3        94.20          3.26          3.07          0.19       1.92   \n",
              "4        94.10          3.27          3.08          0.19       1.92   \n",
              "...        ...           ...           ...           ...        ...   \n",
              "420546   73.10          4.52          3.30          1.22       2.06   \n",
              "420547   69.71          4.77          3.32          1.44       2.07   \n",
              "420548   67.91          4.84          3.28          1.55       2.05   \n",
              "420549   71.80          4.46          3.20          1.26       1.99   \n",
              "420550   75.70          4.27          3.23          1.04       2.01   \n",
              "\n",
              "        H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "0                  3.12       1307.75      1.03           1.75     152.3  \n",
              "1                  3.03       1309.80      0.72           1.50     136.1  \n",
              "2                  3.02       1310.24      0.19           0.63     171.6  \n",
              "3                  3.08       1309.19      0.34           0.50     198.0  \n",
              "4                  3.09       1309.00      0.32           0.63     214.3  \n",
              "...                 ...           ...       ...            ...       ...  \n",
              "420546             3.30       1292.98      0.67           1.52     240.0  \n",
              "420547             3.32       1289.44      1.14           1.92     234.3  \n",
              "420548             3.28       1288.39      1.08           2.00     215.2  \n",
              "420549             3.20       1293.56      1.49           2.16     225.8  \n",
              "420550             3.23       1296.38      1.23           1.96     184.9  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
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              "    <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>93.30</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.11</td>\n",
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              "      <td>1.94</td>\n",
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              "      <td>1307.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1.75</td>\n",
              "      <td>152.3</td>\n",
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              "    <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>93.40</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",
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              "    <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>93.90</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",
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              "    <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>94.20</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",
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              "    <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>94.10</td>\n",
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              "      <td>3.09</td>\n",
              "      <td>1309.00</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.63</td>\n",
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              "      <td>31.12.2016 23:20:00</td>\n",
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              "      <td>31.12.2016 23:30:00</td>\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb7f1180-cecd-4351-b732-cc2bdc683191 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[5::6] # Take every 6th only, every hour instead of 10 min intervals\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "aWTCJ4fdxFcJ",
        "outputId": "4de46e59-7d41-47c9-f0ee-c5ab4553e83a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  \\\n",
              "5       01.01.2009 01:00:00    996.50     -8.05    265.38        -8.78   \n",
              "11      01.01.2009 02:00:00    996.62     -8.88    264.54        -9.77   \n",
              "17      01.01.2009 03:00:00    996.84     -8.81    264.59        -9.66   \n",
              "23      01.01.2009 04:00:00    996.99     -9.05    264.34       -10.02   \n",
              "29      01.01.2009 05:00:00    997.46     -9.63    263.72       -10.65   \n",
              "...                     ...       ...       ...       ...          ...   \n",
              "420521  31.12.2016 19:10:00   1002.18     -0.98    272.01        -5.36   \n",
              "420527  31.12.2016 20:10:00   1001.40     -1.40    271.66        -6.84   \n",
              "420533  31.12.2016 21:10:00   1001.19     -2.75    270.32        -6.90   \n",
              "420539  31.12.2016 22:10:00   1000.65     -2.89    270.22        -7.15   \n",
              "420545  31.12.2016 23:10:00   1000.11     -3.93    269.23        -8.09   \n",
              "\n",
              "        rh (%)  VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  \\\n",
              "5        94.40          3.33          3.14          0.19       1.96   \n",
              "11       93.20          3.12          2.90          0.21       1.81   \n",
              "17       93.50          3.13          2.93          0.20       1.83   \n",
              "23       92.60          3.07          2.85          0.23       1.78   \n",
              "29       92.20          2.94          2.71          0.23       1.69   \n",
              "...        ...           ...           ...           ...        ...   \n",
              "420521   72.00          5.69          4.09          1.59       2.54   \n",
              "420527   66.29          5.51          3.65          1.86       2.27   \n",
              "420533   72.90          4.99          3.64          1.35       2.26   \n",
              "420539   72.30          4.93          3.57          1.37       2.22   \n",
              "420545   72.60          4.56          3.31          1.25       2.06   \n",
              "\n",
              "        H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "5                  3.15       1307.86      0.21           0.63     192.7  \n",
              "11                 2.91       1312.25      0.25           0.63     190.3  \n",
              "17                 2.94       1312.18      0.18           0.63     167.2  \n",
              "23                 2.85       1313.61      0.10           0.38     240.0  \n",
              "29                 2.71       1317.19      0.40           0.88     157.0  \n",
              "...                 ...           ...       ...            ...       ...  \n",
              "420521             4.08       1280.70      0.87           1.36     190.6  \n",
              "420527             3.65       1281.87      1.02           1.92     225.4  \n",
              "420533             3.63       1288.02      0.71           1.56     158.7  \n",
              "420539             3.57       1288.03      0.35           0.68     216.7  \n",
              "420545             3.31       1292.41      0.56           1.00     202.6  \n",
              "\n",
              "[70091 rows x 15 columns]"
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              "  <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>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>94.40</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>11</th>\n",
              "      <td>01.01.2009 02:00:00</td>\n",
              "      <td>996.62</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>264.54</td>\n",
              "      <td>-9.77</td>\n",
              "      <td>93.20</td>\n",
              "      <td>3.12</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.91</td>\n",
              "      <td>1312.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.63</td>\n",
              "      <td>190.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>01.01.2009 03:00:00</td>\n",
              "      <td>996.84</td>\n",
              "      <td>-8.81</td>\n",
              "      <td>264.59</td>\n",
              "      <td>-9.66</td>\n",
              "      <td>93.50</td>\n",
              "      <td>3.13</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1312.18</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.63</td>\n",
              "      <td>167.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>01.01.2009 04:00:00</td>\n",
              "      <td>996.99</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>264.34</td>\n",
              "      <td>-10.02</td>\n",
              "      <td>92.60</td>\n",
              "      <td>3.07</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1313.61</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.38</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>01.01.2009 05:00:00</td>\n",
              "      <td>997.46</td>\n",
              "      <td>-9.63</td>\n",
              "      <td>263.72</td>\n",
              "      <td>-10.65</td>\n",
              "      <td>92.20</td>\n",
              "      <td>2.94</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.71</td>\n",
              "      <td>1317.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.88</td>\n",
              "      <td>157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420521</th>\n",
              "      <td>31.12.2016 19:10:00</td>\n",
              "      <td>1002.18</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>272.01</td>\n",
              "      <td>-5.36</td>\n",
              "      <td>72.00</td>\n",
              "      <td>5.69</td>\n",
              "      <td>4.09</td>\n",
              "      <td>1.59</td>\n",
              "      <td>2.54</td>\n",
              "      <td>4.08</td>\n",
              "      <td>1280.70</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.36</td>\n",
              "      <td>190.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420527</th>\n",
              "      <td>31.12.2016 20:10:00</td>\n",
              "      <td>1001.40</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>271.66</td>\n",
              "      <td>-6.84</td>\n",
              "      <td>66.29</td>\n",
              "      <td>5.51</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.27</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1281.87</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.92</td>\n",
              "      <td>225.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420533</th>\n",
              "      <td>31.12.2016 21:10:00</td>\n",
              "      <td>1001.19</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>270.32</td>\n",
              "      <td>-6.90</td>\n",
              "      <td>72.90</td>\n",
              "      <td>4.99</td>\n",
              "      <td>3.64</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.26</td>\n",
              "      <td>3.63</td>\n",
              "      <td>1288.02</td>\n",
              "      <td>0.71</td>\n",
              "      <td>1.56</td>\n",
              "      <td>158.7</td>\n",
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              "    <tr>\n",
              "      <th>420539</th>\n",
              "      <td>31.12.2016 22:10:00</td>\n",
              "      <td>1000.65</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>270.22</td>\n",
              "      <td>-7.15</td>\n",
              "      <td>72.30</td>\n",
              "      <td>4.93</td>\n",
              "      <td>3.57</td>\n",
              "      <td>1.37</td>\n",
              "      <td>2.22</td>\n",
              "      <td>3.57</td>\n",
              "      <td>1288.03</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.68</td>\n",
              "      <td>216.7</td>\n",
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              "    <tr>\n",
              "      <th>420545</th>\n",
              "      <td>31.12.2016 23:10:00</td>\n",
              "      <td>1000.11</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>269.23</td>\n",
              "      <td>-8.09</td>\n",
              "      <td>72.60</td>\n",
              "      <td>4.56</td>\n",
              "      <td>3.31</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.06</td>\n",
              "      <td>3.31</td>\n",
              "      <td>1292.41</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.00</td>\n",
              "      <td>202.6</td>\n",
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              "  </tbody>\n",
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              "<p>70091 rows × 15 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = pd.to_datetime(df['Date Time'], format='%d.%m.%Y %H:%M:%S')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "POQ_793cxYOY",
        "outputId": "246ba93c-a3fe-4a32-974d-8d5b7ea3b098"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Date Time  p (mbar)  T (degC)  Tpot (K)  \\\n",
              "Date Time                                                                \n",
              "2009-01-01 01:00:00  01.01.2009 01:00:00    996.50     -8.05    265.38   \n",
              "2009-01-01 02:00:00  01.01.2009 02:00:00    996.62     -8.88    264.54   \n",
              "2009-01-01 03:00:00  01.01.2009 03:00:00    996.84     -8.81    264.59   \n",
              "2009-01-01 04:00:00  01.01.2009 04:00:00    996.99     -9.05    264.34   \n",
              "2009-01-01 05:00:00  01.01.2009 05:00:00    997.46     -9.63    263.72   \n",
              "...                                  ...       ...       ...       ...   \n",
              "2016-12-31 19:10:00  31.12.2016 19:10:00   1002.18     -0.98    272.01   \n",
              "2016-12-31 20:10:00  31.12.2016 20:10:00   1001.40     -1.40    271.66   \n",
              "2016-12-31 21:10:00  31.12.2016 21:10:00   1001.19     -2.75    270.32   \n",
              "2016-12-31 22:10:00  31.12.2016 22:10:00   1000.65     -2.89    270.22   \n",
              "2016-12-31 23:10:00  31.12.2016 23:10:00   1000.11     -3.93    269.23   \n",
              "\n",
              "                     Tdew (degC)  rh (%)  VPmax (mbar)  VPact (mbar)  \\\n",
              "Date Time                                                              \n",
              "2009-01-01 01:00:00        -8.78   94.40          3.33          3.14   \n",
              "2009-01-01 02:00:00        -9.77   93.20          3.12          2.90   \n",
              "2009-01-01 03:00:00        -9.66   93.50          3.13          2.93   \n",
              "2009-01-01 04:00:00       -10.02   92.60          3.07          2.85   \n",
              "2009-01-01 05:00:00       -10.65   92.20          2.94          2.71   \n",
              "...                          ...     ...           ...           ...   \n",
              "2016-12-31 19:10:00        -5.36   72.00          5.69          4.09   \n",
              "2016-12-31 20:10:00        -6.84   66.29          5.51          3.65   \n",
              "2016-12-31 21:10:00        -6.90   72.90          4.99          3.64   \n",
              "2016-12-31 22:10:00        -7.15   72.30          4.93          3.57   \n",
              "2016-12-31 23:10:00        -8.09   72.60          4.56          3.31   \n",
              "\n",
              "                     VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
              "Date Time                                                                     \n",
              "2009-01-01 01:00:00          0.19       1.96             3.15       1307.86   \n",
              "2009-01-01 02:00:00          0.21       1.81             2.91       1312.25   \n",
              "2009-01-01 03:00:00          0.20       1.83             2.94       1312.18   \n",
              "2009-01-01 04:00:00          0.23       1.78             2.85       1313.61   \n",
              "2009-01-01 05:00:00          0.23       1.69             2.71       1317.19   \n",
              "...                           ...        ...              ...           ...   \n",
              "2016-12-31 19:10:00          1.59       2.54             4.08       1280.70   \n",
              "2016-12-31 20:10:00          1.86       2.27             3.65       1281.87   \n",
              "2016-12-31 21:10:00          1.35       2.26             3.63       1288.02   \n",
              "2016-12-31 22:10:00          1.37       2.22             3.57       1288.03   \n",
              "2016-12-31 23:10:00          1.25       2.06             3.31       1292.41   \n",
              "\n",
              "                     wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "Date Time                                               \n",
              "2009-01-01 01:00:00      0.21           0.63     192.7  \n",
              "2009-01-01 02:00:00      0.25           0.63     190.3  \n",
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              "2009-01-01 04:00:00      0.10           0.38     240.0  \n",
              "2009-01-01 05:00:00      0.40           0.88     157.0  \n",
              "...                       ...            ...       ...  \n",
              "2016-12-31 19:10:00      0.87           1.36     190.6  \n",
              "2016-12-31 20:10:00      1.02           1.92     225.4  \n",
              "2016-12-31 21:10:00      0.71           1.56     158.7  \n",
              "2016-12-31 22:10:00      0.35           0.68     216.7  \n",
              "2016-12-31 23:10:00      0.56           1.00     202.6  \n",
              "\n",
              "[70091 rows x 15 columns]"
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              "      <th>Date Time</th>\n",
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              "      <th>2009-01-01 01:00:00</th>\n",
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              "      <td>0.63</td>\n",
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              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\n",
              "      <td>01.01.2009 02:00:00</td>\n",
              "      <td>996.62</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>264.54</td>\n",
              "      <td>-9.77</td>\n",
              "      <td>93.20</td>\n",
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              "      <td>190.3</td>\n",
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              "    <tr>\n",
              "      <th>2009-01-01 03:00:00</th>\n",
              "      <td>01.01.2009 03:00:00</td>\n",
              "      <td>996.84</td>\n",
              "      <td>-8.81</td>\n",
              "      <td>264.59</td>\n",
              "      <td>-9.66</td>\n",
              "      <td>93.50</td>\n",
              "      <td>3.13</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1312.18</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.63</td>\n",
              "      <td>167.2</td>\n",
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              "      <th>2009-01-01 04:00:00</th>\n",
              "      <td>01.01.2009 04:00:00</td>\n",
              "      <td>996.99</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>264.34</td>\n",
              "      <td>-10.02</td>\n",
              "      <td>92.60</td>\n",
              "      <td>3.07</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1313.61</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.38</td>\n",
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              "      <td>997.46</td>\n",
              "      <td>-9.63</td>\n",
              "      <td>263.72</td>\n",
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              "      <td>92.20</td>\n",
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              "      <th>2016-12-31 20:10:00</th>\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e2925958-b10d-490b-a375-54b5482d998c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e2925958-b10d-490b-a375-54b5482d998c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-10240e4a-bd23-433f-b768-02208d70c208\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10240e4a-bd23-433f-b768-02208d70c208')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-10240e4a-bd23-433f-b768-02208d70c208 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp = df['T (degC)']\n",
        "temp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "x7VFH_R_xze5",
        "outputId": "6351c487-429e-4dd4-c02c-a478edf12a88"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date Time'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_X_y(df, window_size=5):\n",
        "  \"\"\"\n",
        "  Converts a dataframe to X and y numpy arrays where you observe a window size\n",
        "  and then have the next value as y.\n",
        "\n",
        "  Example:\n",
        "                 X               Y\n",
        "  # [[[1], [2], [3], [4], [5]]] [6]\n",
        "  # [[[2], [3], [4], [5], [6]]] [7]\n",
        "  # [[[3], [4], [5], [6], [7]]] [8]\n",
        "  \"\"\"\n",
        "  df_as_np = df.to_numpy()\n",
        "  X = []\n",
        "  y = []\n",
        "  for i in range(len(df_as_np)-window_size):\n",
        "    row = [[a] for a in df_as_np[i:i+5]]\n",
        "    X.append(row)\n",
        "    label = df_as_np[i+5]\n",
        "    y.append(label)\n",
        "  return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "juvov1XzyFsg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "WINDOW_SIZE=5\n",
        "X, y = df_to_X_y(temp, WINDOW_SIZE)\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjiVIGMg1LRN",
        "outputId": "25ca5c5a-16f7-4a3e-bd44-ad234d00c603"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70086, 5, 1), (70086,))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = X[:60000], y[:60000]\n",
        "X_val, y_val = X[60000:65000], y[60000:65000]\n",
        "X_test, y_test = X[65000:], y[65000:]\n",
        "X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U79FClrQ1nb2",
        "outputId": "7ca55924-9ba4-4b2d-dcaa-d8c81e2420dc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 5, 1), (60000,), (5000, 5, 1), (5000,), (5086, 5, 1), (5086,))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating and training a baseline model"
      ],
      "metadata": {
        "id": "6UcPAg_i8S7h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.losses import MeanSquaredError\n",
        "from tensorflow.keras.metrics import RootMeanSquaredError\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "model = Sequential()\n",
        "model.add(InputLayer((5, 1)))\n",
        "model.add(LSTM(64))\n",
        "model.add(Dense(8, 'relu'))\n",
        "model.add(Dense(1, 'linear'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-Fvbq_S2Ljm",
        "outputId": "f17b5391-b77a-4478-9cb5-d527436f283d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 64)                16896     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17425 (68.07 KB)\n",
            "Trainable params: 17425 (68.07 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp = ModelCheckpoint('model/', save_best_only=True)\n",
        "model.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "GIOFMLz73S1j"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, callbacks=[cp])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQwtH1cY33qj",
        "outputId": "02efdd14-40d4-4b4b-8ad6-7c6a252f97c2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 19s 6ms/step - loss: 27.9810 - root_mean_squared_error: 5.2897 - val_loss: 1.0710 - val_root_mean_squared_error: 1.0349\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 1.7010 - root_mean_squared_error: 1.3042 - val_loss: 0.5973 - val_root_mean_squared_error: 0.7729\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.8630 - root_mean_squared_error: 0.9290 - val_loss: 0.5361 - val_root_mean_squared_error: 0.7322\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.7172 - root_mean_squared_error: 0.8469 - val_loss: 0.5079 - val_root_mean_squared_error: 0.7126\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.6775 - root_mean_squared_error: 0.8231 - val_loss: 0.5067 - val_root_mean_squared_error: 0.7118\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6633 - root_mean_squared_error: 0.8144 - val_loss: 0.5087 - val_root_mean_squared_error: 0.7132\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.6554 - root_mean_squared_error: 0.8095 - val_loss: 0.4924 - val_root_mean_squared_error: 0.7017\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6524 - root_mean_squared_error: 0.8077 - val_loss: 0.5048 - val_root_mean_squared_error: 0.7105\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.6477 - root_mean_squared_error: 0.8048 - val_loss: 0.4857 - val_root_mean_squared_error: 0.6969\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.6457 - root_mean_squared_error: 0.8036 - val_loss: 0.4987 - val_root_mean_squared_error: 0.7062\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799fdc9391b0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model and making predictions"
      ],
      "metadata": {
        "id": "LQxpFlrt8ZtK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('model/')"
      ],
      "metadata": {
        "id": "XbcqoPV54k-1"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "def plot_predictions(model, X, y, start=0, end=100):\n",
        "  predictions = model.predict(X).flatten()\n",
        "  df = pd.DataFrame(data={'Predictions': predictions, 'Actuals':y})\n",
        "  plt.plot(df['Predictions'][start:end])\n",
        "  plt.plot(df['Actuals'][start:end])\n",
        "  return df, mse(predictions, y)"
      ],
      "metadata": {
        "id": "m2AXDO6s_a3i"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making predictions on the training set"
      ],
      "metadata": {
        "id": "o6lkLtpd8gGi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model, X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "IPC9l3yJ_llB",
        "outputId": "5e5c277f-7da0-4d01-d3ba-ffd1f3ff55a0"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 4s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(       Predictions  Actuals\n",
              " 0        -9.816556    -9.67\n",
              " 1        -9.615195    -9.17\n",
              " 2        -8.789055    -8.10\n",
              " 3        -7.256079    -7.66\n",
              " 4        -7.170328    -7.04\n",
              " ...            ...      ...\n",
              " 59995     5.976571     6.07\n",
              " 59996     7.213630     9.88\n",
              " 59997    12.110774    13.53\n",
              " 59998    15.850551    15.43\n",
              " 59999    16.243643    15.54\n",
              " \n",
              " [60000 rows x 2 columns],\n",
              " 0.6467452401517075)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making predictions on the validation set"
      ],
      "metadata": {
        "id": "ketpVt0v8rX1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model, X_val, y_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "tfXcvmkh_k4p",
        "outputId": "b22f1727-9d05-47ac-be06-bf3e0a775b07"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       15.298235    14.02\n",
              " 1       13.197478    13.67\n",
              " 2       13.055926    12.27\n",
              " 3       11.480764    11.19\n",
              " 4       10.390044    10.85\n",
              " ...           ...      ...\n",
              " 4995    17.422876    18.27\n",
              " 4996    17.359745    17.85\n",
              " 4997    17.114061    16.65\n",
              " 4998    15.897094    15.85\n",
              " 4999    15.070779    15.09\n",
              " \n",
              " [5000 rows x 2 columns],\n",
              " 0.48567429871357165)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making predictions on the test set"
      ],
      "metadata": {
        "id": "7XmlamZ08toz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "oT7C8l3K_j0K",
        "outputId": "34dadde2-a48f-4c6f-c573-fb0576d2fc6e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       14.396859    13.99\n",
              " 1       13.287561    13.46\n",
              " 2       12.888417    12.93\n",
              " 3       12.472287    12.43\n",
              " 4       12.027122    12.17\n",
              " ...           ...      ...\n",
              " 5081    -1.239725    -0.98\n",
              " 5082    -1.460385    -1.40\n",
              " 5083    -1.640144    -2.75\n",
              " 5084    -3.252397    -2.89\n",
              " 5085    -3.110189    -3.93\n",
              " \n",
              " [5086 rows x 2 columns],\n",
              " 0.6303741635968759)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: 1D CNN"
      ],
      "metadata": {
        "id": "5UCNMQ6kCCQd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1d CNN\n",
        "model2 = Sequential()\n",
        "model2.add(InputLayer((5, 1)))\n",
        "model2.add(Conv1D(64, kernel_size=2, activation='relu'))\n",
        "model2.add(Flatten())\n",
        "model2.add(Dense(8, 'relu'))\n",
        "model2.add(Dense(1, 'linear'))\n",
        "model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fBvlM3t_K68",
        "outputId": "1e786e47-ee9c-4bde-d27a-2b0a81e40e98"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 4, 64)             192       \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 8)                 2056      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2257 (8.82 KB)\n",
            "Trainable params: 2257 (8.82 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp2 = ModelCheckpoint('model2/', save_best_only=True)\n",
        "model2.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "4GoKbujBBd2l"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, callbacks=[cp2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3QZeLs9Bipr",
        "outputId": "cb9552a5-e882-4ff5-e88e-9f4327477baf"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 13s 4ms/step - loss: 7.5089 - root_mean_squared_error: 2.7402 - val_loss: 0.9948 - val_root_mean_squared_error: 0.9974\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.9966 - root_mean_squared_error: 0.9983 - val_loss: 0.6386 - val_root_mean_squared_error: 0.7991\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7559 - root_mean_squared_error: 0.8694 - val_loss: 0.5418 - val_root_mean_squared_error: 0.7361\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6961 - root_mean_squared_error: 0.8343 - val_loss: 0.5194 - val_root_mean_squared_error: 0.7207\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6787 - root_mean_squared_error: 0.8239 - val_loss: 0.4996 - val_root_mean_squared_error: 0.7068\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6686 - root_mean_squared_error: 0.8177 - val_loss: 0.4923 - val_root_mean_squared_error: 0.7016\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6656 - root_mean_squared_error: 0.8159 - val_loss: 0.4885 - val_root_mean_squared_error: 0.6990\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6626 - root_mean_squared_error: 0.8140 - val_loss: 0.5134 - val_root_mean_squared_error: 0.7165\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6597 - root_mean_squared_error: 0.8122 - val_loss: 0.5081 - val_root_mean_squared_error: 0.7128\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6540 - root_mean_squared_error: 0.8087 - val_loss: 0.4869 - val_root_mean_squared_error: 0.6978\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799fdd47bd90>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model2, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "tTdgq_ZmBoNT",
        "outputId": "578ba583-c7ba-41a1-fe3b-44ffa302eb5f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       14.320770    13.99\n",
              " 1       13.223012    13.46\n",
              " 2       12.896441    12.93\n",
              " 3       12.440392    12.43\n",
              " 4       12.008271    12.17\n",
              " ...           ...      ...\n",
              " 5081    -1.260059    -0.98\n",
              " 5082    -1.454574    -1.40\n",
              " 5083    -1.667148    -2.75\n",
              " 5084    -3.012902    -2.89\n",
              " 5085    -3.260167    -3.93\n",
              " \n",
              " [5086 rows x 2 columns],\n",
              " 0.6206021314268491)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We probably prefer the CNN model over the LSTM model as it is smaller and has a lower test MSE."
      ],
      "metadata": {
        "id": "H9lATZpvE65s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 3: Gated Recurrent Unit (GRU)"
      ],
      "metadata": {
        "id": "biaAmObmCMDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GRU\n",
        "model3 = Sequential()\n",
        "model3.add(InputLayer((5, 1)))\n",
        "model3.add(GRU(64))\n",
        "model3.add(Dense(8, 'relu'))\n",
        "model3.add(Dense(1, 'linear'))\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inbK70auB14f",
        "outputId": "7fe00003-b43f-4883-a06b-79c5eb3df99a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 64)                12864     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 13393 (52.32 KB)\n",
            "Trainable params: 13393 (52.32 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp3 = ModelCheckpoint('model3/', save_best_only=True)\n",
        "model3.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "gBjMfOPBEZ01"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, callbacks=[cp3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7d8xCVEEcCg",
        "outputId": "516b4b8f-3c6b-4d5b-8956-9241ceabb6a7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 13s 6ms/step - loss: 37.1179 - root_mean_squared_error: 6.0924 - val_loss: 1.0317 - val_root_mean_squared_error: 1.0157\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1.8434 - root_mean_squared_error: 1.3577 - val_loss: 0.6128 - val_root_mean_squared_error: 0.7828\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.8653 - root_mean_squared_error: 0.9302 - val_loss: 0.5248 - val_root_mean_squared_error: 0.7244\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.7051 - root_mean_squared_error: 0.8397 - val_loss: 0.5023 - val_root_mean_squared_error: 0.7087\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.6648 - root_mean_squared_error: 0.8153 - val_loss: 0.5001 - val_root_mean_squared_error: 0.7072\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.6539 - root_mean_squared_error: 0.8086 - val_loss: 0.4935 - val_root_mean_squared_error: 0.7025\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.6477 - root_mean_squared_error: 0.8048 - val_loss: 0.4967 - val_root_mean_squared_error: 0.7047\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.6438 - root_mean_squared_error: 0.8024 - val_loss: 0.4974 - val_root_mean_squared_error: 0.7053\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.6416 - root_mean_squared_error: 0.8010 - val_loss: 0.4888 - val_root_mean_squared_error: 0.6991\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.6402 - root_mean_squared_error: 0.8001 - val_loss: 0.4853 - val_root_mean_squared_error: 0.6966\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799fdd21f0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model3, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "XRt9Xqh8FJWC",
        "outputId": "4f113f21-c1b1-4b6e-9ab7-3c49b8645be3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       14.418683    13.99\n",
              " 1       13.254105    13.46\n",
              " 2       12.877055    12.93\n",
              " 3       12.445002    12.43\n",
              " 4       11.994130    12.17\n",
              " ...           ...      ...\n",
              " 5081    -1.228414    -0.98\n",
              " 5082    -1.458687    -1.40\n",
              " 5083    -1.611170    -2.75\n",
              " 5084    -3.091632    -2.89\n",
              " 5085    -3.000831    -3.93\n",
              " \n",
              " [5086 rows x 2 columns],\n",
              " 0.6348920297155334)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The CNN model is still the best one on the test set with the lowest MSE and the smallest size."
      ],
      "metadata": {
        "id": "N-XofwD0FNtR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using more variables (day/year sin/cos)"
      ],
      "metadata": {
        "id": "zQmA34cfEkGW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = pd.DataFrame({'Temperature':temp})\n",
        "temp_df['Seconds'] = temp_df.index.map(pd.Timestamp.timestamp)\n",
        "temp_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "oVMEITiFEwHp",
        "outputId": "5cf5a9c9-b3e8-40c1-dbd6-fbd26492e0cc"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Temperature       Seconds\n",
              "Date Time                                     \n",
              "2009-01-01 01:00:00        -8.05  1.230772e+09\n",
              "2009-01-01 02:00:00        -8.88  1.230775e+09\n",
              "2009-01-01 03:00:00        -8.81  1.230779e+09\n",
              "2009-01-01 04:00:00        -9.05  1.230782e+09\n",
              "2009-01-01 05:00:00        -9.63  1.230786e+09\n",
              "...                          ...           ...\n",
              "2016-12-31 19:10:00        -0.98  1.483211e+09\n",
              "2016-12-31 20:10:00        -1.40  1.483215e+09\n",
              "2016-12-31 21:10:00        -2.75  1.483219e+09\n",
              "2016-12-31 22:10:00        -2.89  1.483222e+09\n",
              "2016-12-31 23:10:00        -3.93  1.483226e+09\n",
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          "metadata": {},
          "execution_count": 43
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    },
    {
      "cell_type": "code",
      "source": [
        "day = 60*60*24 # Number of seconds in a day\n",
        "year = (365.2425)*day # Number of seconds in a year\n",
        "\n",
        "temp_df['Day sin'] = np.sin(temp_df['Seconds'] * (2 * np.pi / day))\n",
        "temp_df['Day cos'] = np.cos(temp_df['Seconds'] * (2 * np.pi / day))\n",
        "temp_df['Year sin'] = np.sin(temp_df['Seconds'] * (2 * np.pi / year))\n",
        "temp_df['Year cos'] = np.cos(temp_df['Seconds'] * (2 * np.pi / year))\n",
        "temp_df"
      ],
      "metadata": {
        "colab": {
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        "id": "9h1vaOJkF5Hl",
        "outputId": "e66791b2-8545-45f3-ef4e-de1f1c619e53"
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      "execution_count": 44,
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        {
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              "                     Temperature       Seconds   Day sin   Day cos  Year sin  \\\n",
              "Date Time                                                                      \n",
              "2009-01-01 01:00:00        -8.05  1.230772e+09  0.258819  0.965926  0.010049   \n",
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              "2009-01-01 03:00:00        -8.81  1.230779e+09  0.707107  0.707107  0.011483   \n",
              "2009-01-01 04:00:00        -9.05  1.230782e+09  0.866025  0.500000  0.012199   \n",
              "2009-01-01 05:00:00        -9.63  1.230786e+09  0.965926  0.258819  0.012916   \n",
              "...                          ...           ...       ...       ...       ...   \n",
              "2016-12-31 19:10:00        -0.98  1.483211e+09 -0.953717  0.300706  0.006900   \n",
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              "2016-12-31 23:10:00        -3.93  1.483226e+09 -0.216440  0.976296  0.009767   \n",
              "\n",
              "                     Year cos  \n",
              "Date Time                      \n",
              "2009-01-01 01:00:00  0.999950  \n",
              "2009-01-01 02:00:00  0.999942  \n",
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              "2009-01-01 05:00:00  0.999917  \n",
              "...                       ...  \n",
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              "2016-12-31 20:10:00  0.999971  \n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-504c7dfe-f85b-4611-8c9b-ebd269a4fc8f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-504c7dfe-f85b-4611-8c9b-ebd269a4fc8f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dd0413ed-450b-4724-9ffd-9f4481c20e8b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd0413ed-450b-4724-9ffd-9f4481c20e8b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dd0413ed-450b-4724-9ffd-9f4481c20e8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = temp_df.drop('Seconds', axis=1)\n",
        "temp_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "unFPPc97GTHh",
        "outputId": "db236cc2-d5dd-4469-d963-80c3a3431a44"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Temperature   Day sin   Day cos  Year sin  Year cos\n",
              "Date Time                                                               \n",
              "2009-01-01 01:00:00        -8.05  0.258819  0.965926  0.010049  0.999950\n",
              "2009-01-01 02:00:00        -8.88  0.500000  0.866025  0.010766  0.999942\n",
              "2009-01-01 03:00:00        -8.81  0.707107  0.707107  0.011483  0.999934\n",
              "2009-01-01 04:00:00        -9.05  0.866025  0.500000  0.012199  0.999926\n",
              "2009-01-01 05:00:00        -9.63  0.965926  0.258819  0.012916  0.999917\n",
              "...                          ...       ...       ...       ...       ...\n",
              "2016-12-31 19:10:00        -0.98 -0.953717  0.300706  0.006900  0.999976\n",
              "2016-12-31 20:10:00        -1.40 -0.843391  0.537300  0.007617  0.999971\n",
              "2016-12-31 21:10:00        -2.75 -0.675590  0.737277  0.008334  0.999965\n",
              "2016-12-31 22:10:00        -2.89 -0.461749  0.887011  0.009050  0.999959\n",
              "2016-12-31 23:10:00        -3.93 -0.216440  0.976296  0.009767  0.999952\n",
              "\n",
              "[70091 rows x 5 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Day sin</th>\n",
              "      <th>Day cos</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Year cos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-01-01 01:00:00</th>\n",
              "      <td>-8.05</td>\n",
              "      <td>0.258819</td>\n",
              "      <td>0.965926</td>\n",
              "      <td>0.010049</td>\n",
              "      <td>0.999950</td>\n",
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              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\n",
              "      <td>-8.88</td>\n",
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              "      <td>0.010766</td>\n",
              "      <td>0.999942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 03:00:00</th>\n",
              "      <td>-8.81</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>0.011483</td>\n",
              "      <td>0.999934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 04:00:00</th>\n",
              "      <td>-9.05</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.012199</td>\n",
              "      <td>0.999926</td>\n",
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              "    <tr>\n",
              "      <th>2009-01-01 05:00:00</th>\n",
              "      <td>-9.63</td>\n",
              "      <td>0.965926</td>\n",
              "      <td>0.258819</td>\n",
              "      <td>0.012916</td>\n",
              "      <td>0.999917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 19:10:00</th>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.953717</td>\n",
              "      <td>0.300706</td>\n",
              "      <td>0.006900</td>\n",
              "      <td>0.999976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:10:00</th>\n",
              "      <td>-1.40</td>\n",
              "      <td>-0.843391</td>\n",
              "      <td>0.537300</td>\n",
              "      <td>0.007617</td>\n",
              "      <td>0.999971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:10:00</th>\n",
              "      <td>-2.75</td>\n",
              "      <td>-0.675590</td>\n",
              "      <td>0.737277</td>\n",
              "      <td>0.008334</td>\n",
              "      <td>0.999965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:10:00</th>\n",
              "      <td>-2.89</td>\n",
              "      <td>-0.461749</td>\n",
              "      <td>0.887011</td>\n",
              "      <td>0.009050</td>\n",
              "      <td>0.999959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:10:00</th>\n",
              "      <td>-3.93</td>\n",
              "      <td>-0.216440</td>\n",
              "      <td>0.976296</td>\n",
              "      <td>0.009767</td>\n",
              "      <td>0.999952</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70091 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-846fdf68-0a20-4057-a8e0-081dd6691900 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-846fdf68-0a20-4057-a8e0-081dd6691900');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "     width=\"24px\">\n",
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              "\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e044786d-e129-49a9-8fe3-65321636d327 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_X_y2(df, window_size=6): # Increasing the window size compared to the function above\n",
        "  df_as_np = df.to_numpy()\n",
        "  X = []\n",
        "  y = []\n",
        "  for i in range(len(df_as_np)-window_size):\n",
        "    row = [r for r in df_as_np[i:i+window_size]]\n",
        "    X.append(row)\n",
        "    label = df_as_np[i+window_size][0]\n",
        "    y.append(label)\n",
        "  return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "_a5B2QvIGWfM"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X2, y2 = df_to_X_y2(temp_df)\n",
        "X2.shape, y2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjBE0tW7Gb-7",
        "outputId": "869d3450-3c64-4568-f267-db2165f4e218"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70085, 6, 5), (70085,))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X2_train, y2_train = X2[:60000], y2[:60000]\n",
        "X2_val, y2_val = X2[60000:65000], y2[60000:65000]\n",
        "X2_test, y2_test = X2[65000:], y2[65000:]\n",
        "X2_train.shape, y2_train.shape, X2_val.shape, y2_val.shape, X2_test.shape, y2_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQAOfg_rGd4l",
        "outputId": "a152e4d1-8bc3-4e0b-b0a7-0ce37d3bb707"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 6, 5), (60000,), (5000, 6, 5), (5000,), (5085, 6, 5), (5085,))"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_training_mean = np.mean(X2_train[:, :, 0])\n",
        "temp_training_std = np.std(X2_train[:, :, 0])\n",
        "\n",
        "def preprocess(X):\n",
        "  X[:, :, 0] = (X[:, :, 0] - temp_training_mean) / temp_training_std\n",
        "  return X"
      ],
      "metadata": {
        "id": "qwaL8Mb4GgKK"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess(X2_train)\n",
        "preprocess(X2_val)\n",
        "preprocess(X2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDrmdco0GiWY",
        "outputId": "9ebfef47-b716-4eef-b564-4d000b20f9bf"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 1.04009706e+00, -9.65925826e-01,  2.58819045e-01,\n",
              "          5.22877889e-01, -8.52407598e-01],\n",
              "        [ 9.90732794e-01, -8.66025404e-01,  5.00000000e-01,\n",
              "          5.22266764e-01, -8.52782169e-01],\n",
              "        [ 8.49692028e-01, -7.07106781e-01,  7.07106781e-01,\n",
              "          5.21655371e-01, -8.53156301e-01],\n",
              "        [ 7.55664851e-01, -5.00000000e-01,  8.66025404e-01,\n",
              "          5.21043709e-01, -8.53529995e-01],\n",
              "        [ 6.66339032e-01, -2.58819045e-01,  9.65925826e-01,\n",
              "          5.20431780e-01, -8.53903251e-01],\n",
              "        [ 5.37051663e-01, -7.16474371e-12,  1.00000000e+00,\n",
              "          5.19819584e-01, -8.54276068e-01]],\n",
              "\n",
              "       [[ 9.90732794e-01, -8.66025404e-01,  5.00000000e-01,\n",
              "          5.22266764e-01, -8.52782169e-01],\n",
              "        [ 8.49692028e-01, -7.07106781e-01,  7.07106781e-01,\n",
              "          5.21655371e-01, -8.53156301e-01],\n",
              "        [ 7.55664851e-01, -5.00000000e-01,  8.66025404e-01,\n",
              "          5.21043709e-01, -8.53529995e-01],\n",
              "        [ 6.66339032e-01, -2.58819045e-01,  9.65925826e-01,\n",
              "          5.20431780e-01, -8.53903251e-01],\n",
              "        [ 5.37051663e-01, -7.16474371e-12,  1.00000000e+00,\n",
              "          5.19819584e-01, -8.54276068e-01],\n",
              "        [ 4.74758658e-01,  2.58819045e-01,  9.65925826e-01,\n",
              "          5.19207120e-01, -8.54648446e-01]],\n",
              "\n",
              "       [[ 8.49692028e-01, -7.07106781e-01,  7.07106781e-01,\n",
              "          5.21655371e-01, -8.53156301e-01],\n",
              "        [ 7.55664851e-01, -5.00000000e-01,  8.66025404e-01,\n",
              "          5.21043709e-01, -8.53529995e-01],\n",
              "        [ 6.66339032e-01, -2.58819045e-01,  9.65925826e-01,\n",
              "          5.20431780e-01, -8.53903251e-01],\n",
              "        [ 5.37051663e-01, -7.16474371e-12,  1.00000000e+00,\n",
              "          5.19819584e-01, -8.54276068e-01],\n",
              "        [ 4.74758658e-01,  2.58819045e-01,  9.65925826e-01,\n",
              "          5.19207120e-01, -8.54648446e-01],\n",
              "        [ 4.12465653e-01,  5.00000000e-01,  8.66025404e-01,\n",
              "          5.18594390e-01, -8.55020385e-01]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[-5.23104762e-01, -7.37277337e-01, -6.75590208e-01,\n",
              "          4.03308458e-03,  9.99991867e-01],\n",
              "        [-8.20465711e-01, -8.87010833e-01, -4.61748613e-01,\n",
              "          4.74986003e-03,  9.99988719e-01],\n",
              "        [-9.55629779e-01, -9.76296007e-01, -2.16439614e-01,\n",
              "          5.46663304e-03,  9.99985058e-01],\n",
              "        [-1.15661287e+00, -9.99048222e-01,  4.36193874e-02,\n",
              "          6.18340325e-03,  9.99980883e-01],\n",
              "        [-1.22243189e+00, -9.53716951e-01,  3.00705799e-01,\n",
              "          6.90017027e-03,  9.99976194e-01],\n",
              "        [-1.27179616e+00, -8.43391446e-01,  5.37299608e-01,\n",
              "          7.61693375e-03,  9.99970991e-01]],\n",
              "\n",
              "       [[-8.20465711e-01, -8.87010833e-01, -4.61748613e-01,\n",
              "          4.74986003e-03,  9.99988719e-01],\n",
              "        [-9.55629779e-01, -9.76296007e-01, -2.16439614e-01,\n",
              "          5.46663304e-03,  9.99985058e-01],\n",
              "        [-1.15661287e+00, -9.99048222e-01,  4.36193874e-02,\n",
              "          6.18340325e-03,  9.99980883e-01],\n",
              "        [-1.22243189e+00, -9.53716951e-01,  3.00705799e-01,\n",
              "          6.90017027e-03,  9.99976194e-01],\n",
              "        [-1.27179616e+00, -8.43391446e-01,  5.37299608e-01,\n",
              "          7.61693375e-03,  9.99970991e-01],\n",
              "        [-1.43046702e+00, -6.75590208e-01,  7.37277337e-01,\n",
              "          8.33369332e-03,  9.99965274e-01]],\n",
              "\n",
              "       [[-9.55629779e-01, -9.76296007e-01, -2.16439614e-01,\n",
              "          5.46663304e-03,  9.99985058e-01],\n",
              "        [-1.15661287e+00, -9.99048222e-01,  4.36193874e-02,\n",
              "          6.18340325e-03,  9.99980883e-01],\n",
              "        [-1.22243189e+00, -9.53716951e-01,  3.00705799e-01,\n",
              "          6.90017027e-03,  9.99976194e-01],\n",
              "        [-1.27179616e+00, -8.43391446e-01,  5.37299608e-01,\n",
              "          7.61693375e-03,  9.99970991e-01],\n",
              "        [-1.43046702e+00, -6.75590208e-01,  7.37277337e-01,\n",
              "          8.33369332e-03,  9.99965274e-01],\n",
              "        [-1.44692178e+00, -4.61748613e-01,  8.87010833e-01,\n",
              "          9.05044860e-03,  9.99959044e-01]]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 4: LSTM with day/year sin/cos"
      ],
      "metadata": {
        "id": "UEXB9YGyGo4q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model4 = Sequential()\n",
        "model4.add(InputLayer((6, 5))) # Adding one more time step and 4 more variables\n",
        "model4.add(LSTM(64))\n",
        "model4.add(Dense(8, 'relu'))\n",
        "model4.add(Dense(1, 'linear'))\n",
        "\n",
        "model4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gNRqcMKGwSs",
        "outputId": "996f5f68-c2b9-4229-cefd-59a7c7479554"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_1 (LSTM)               (None, 64)                17920     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18449 (72.07 KB)\n",
            "Trainable params: 18449 (72.07 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp4 = ModelCheckpoint('model4/', save_best_only=True)\n",
        "model4.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "cAX73IkkG0vD"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model4.fit(X2_train, y2_train, validation_data=(X2_val, y2_val), epochs=10, callbacks=[cp4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wrp5k0pqG3LK",
        "outputId": "8e0a4d82-5c88-40cd-acdc-6552cb18a3a1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 14s 6ms/step - loss: 39.6008 - root_mean_squared_error: 6.2929 - val_loss: 3.8983 - val_root_mean_squared_error: 1.9744\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 4.7197 - root_mean_squared_error: 2.1725 - val_loss: 2.0800 - val_root_mean_squared_error: 1.4422\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 2.1718 - root_mean_squared_error: 1.4737 - val_loss: 1.0692 - val_root_mean_squared_error: 1.0340\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1.1770 - root_mean_squared_error: 1.0849 - val_loss: 0.7773 - val_root_mean_squared_error: 0.8816\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.8755 - root_mean_squared_error: 0.9357 - val_loss: 0.6163 - val_root_mean_squared_error: 0.7850\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.7096 - root_mean_squared_error: 0.8424 - val_loss: 0.5086 - val_root_mean_squared_error: 0.7132\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.6042 - root_mean_squared_error: 0.7773 - val_loss: 0.4478 - val_root_mean_squared_error: 0.6692\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.5594 - root_mean_squared_error: 0.7480 - val_loss: 0.4292 - val_root_mean_squared_error: 0.6551\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.5417 - root_mean_squared_error: 0.7360 - val_loss: 0.4331 - val_root_mean_squared_error: 0.6581\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.5331 - root_mean_squared_error: 0.7301 - val_loss: 0.4168 - val_root_mean_squared_error: 0.6456\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799fdd2f1870>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(model4, X2_test, y2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "FargGaXeG4nU",
        "outputId": "50533eca-4e2c-4f5e-d2b3-a4722ffd5e7c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       13.140190    13.46\n",
              " 1       12.717020    12.93\n",
              " 2       12.300428    12.43\n",
              " 3       11.997607    12.17\n",
              " 4       12.111172    11.55\n",
              " ...           ...      ...\n",
              " 5080    -1.119598    -0.98\n",
              " 5081    -1.468333    -1.40\n",
              " 5082    -1.738390    -2.75\n",
              " 5083    -3.138285    -2.89\n",
              " 5084    -3.144817    -3.93\n",
              " \n",
              " [5085 rows x 2 columns],\n",
              " 0.48279482668783036)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding the day/year sin/cos variables seems to provide a quite good performance increase by lowering the test set MSE."
      ],
      "metadata": {
        "id": "mLtQF0bIH_Nl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding p (mbar) as a variable, both as data and as an additional target to predict"
      ],
      "metadata": {
        "id": "1rhb3RtXIz5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_temp_df = pd.concat([df['p (mbar)'], temp_df], axis=1)\n",
        "p_temp_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "YBC8OPDZJ3vk",
        "outputId": "45f689f9-0b4b-46f2-f439-b5c1c71ef148"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     p (mbar)  Temperature   Day sin   Day cos  Year sin  \\\n",
              "Date Time                                                                  \n",
              "2009-01-01 01:00:00    996.50        -8.05  0.258819  0.965926  0.010049   \n",
              "2009-01-01 02:00:00    996.62        -8.88  0.500000  0.866025  0.010766   \n",
              "2009-01-01 03:00:00    996.84        -8.81  0.707107  0.707107  0.011483   \n",
              "2009-01-01 04:00:00    996.99        -9.05  0.866025  0.500000  0.012199   \n",
              "2009-01-01 05:00:00    997.46        -9.63  0.965926  0.258819  0.012916   \n",
              "...                       ...          ...       ...       ...       ...   \n",
              "2016-12-31 19:10:00   1002.18        -0.98 -0.953717  0.300706  0.006900   \n",
              "2016-12-31 20:10:00   1001.40        -1.40 -0.843391  0.537300  0.007617   \n",
              "2016-12-31 21:10:00   1001.19        -2.75 -0.675590  0.737277  0.008334   \n",
              "2016-12-31 22:10:00   1000.65        -2.89 -0.461749  0.887011  0.009050   \n",
              "2016-12-31 23:10:00   1000.11        -3.93 -0.216440  0.976296  0.009767   \n",
              "\n",
              "                     Year cos  \n",
              "Date Time                      \n",
              "2009-01-01 01:00:00  0.999950  \n",
              "2009-01-01 02:00:00  0.999942  \n",
              "2009-01-01 03:00:00  0.999934  \n",
              "2009-01-01 04:00:00  0.999926  \n",
              "2009-01-01 05:00:00  0.999917  \n",
              "...                       ...  \n",
              "2016-12-31 19:10:00  0.999976  \n",
              "2016-12-31 20:10:00  0.999971  \n",
              "2016-12-31 21:10:00  0.999965  \n",
              "2016-12-31 22:10:00  0.999959  \n",
              "2016-12-31 23:10:00  0.999952  \n",
              "\n",
              "[70091 rows x 6 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Day sin</th>\n",
              "      <th>Day cos</th>\n",
              "      <th>Year sin</th>\n",
              "      <th>Year cos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-01-01 01:00:00</th>\n",
              "      <td>996.50</td>\n",
              "      <td>-8.05</td>\n",
              "      <td>0.258819</td>\n",
              "      <td>0.965926</td>\n",
              "      <td>0.010049</td>\n",
              "      <td>0.999950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\n",
              "      <td>996.62</td>\n",
              "      <td>-8.88</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>0.010766</td>\n",
              "      <td>0.999942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 03:00:00</th>\n",
              "      <td>996.84</td>\n",
              "      <td>-8.81</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>0.011483</td>\n",
              "      <td>0.999934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 04:00:00</th>\n",
              "      <td>996.99</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.012199</td>\n",
              "      <td>0.999926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 05:00:00</th>\n",
              "      <td>997.46</td>\n",
              "      <td>-9.63</td>\n",
              "      <td>0.965926</td>\n",
              "      <td>0.258819</td>\n",
              "      <td>0.012916</td>\n",
              "      <td>0.999917</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>2016-12-31 19:10:00</th>\n",
              "      <td>1002.18</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.953717</td>\n",
              "      <td>0.300706</td>\n",
              "      <td>0.006900</td>\n",
              "      <td>0.999976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:10:00</th>\n",
              "      <td>1001.40</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>-0.843391</td>\n",
              "      <td>0.537300</td>\n",
              "      <td>0.007617</td>\n",
              "      <td>0.999971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:10:00</th>\n",
              "      <td>1001.19</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>-0.675590</td>\n",
              "      <td>0.737277</td>\n",
              "      <td>0.008334</td>\n",
              "      <td>0.999965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:10:00</th>\n",
              "      <td>1000.65</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>-0.461749</td>\n",
              "      <td>0.887011</td>\n",
              "      <td>0.009050</td>\n",
              "      <td>0.999959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:10:00</th>\n",
              "      <td>1000.11</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>-0.216440</td>\n",
              "      <td>0.976296</td>\n",
              "      <td>0.009767</td>\n",
              "      <td>0.999952</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70091 rows × 6 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_X_y3(df, window_size=7): # Increasing window size by 1\n",
        "  df_as_np = df.to_numpy()\n",
        "  X = []\n",
        "  y = []\n",
        "  for i in range(len(df_as_np)-window_size):\n",
        "    row = [r for r in df_as_np[i:i+window_size]]\n",
        "    X.append(row)\n",
        "    label = [df_as_np[i+window_size][0], df_as_np[i+window_size][1]]\n",
        "    y.append(label)\n",
        "  return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "jyPFAGxAJ8up"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X3, y3 = df_to_X_y3(p_temp_df)\n",
        "X3.shape, y3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hl5XOVtiKA-f",
        "outputId": "d92fb511-07bd-4309-e970-46b60304c89b"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70084, 7, 6), (70084, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X3_train, y3_train = X3[:60000], y3[:60000]\n",
        "X3_val, y3_val = X3[60000:65000], y3[60000:65000]\n",
        "X3_test, y3_test = X3[65000:], y3[65000:]\n",
        "X3_train.shape, y3_train.shape, X3_val.shape, y3_val.shape, X3_test.shape, y3_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsIxm8S0KE5M",
        "outputId": "dd7ecbad-ebd1-485a-f0d8-d4ec58068b2e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 7, 6), (60000, 2), (5000, 7, 6), (5000, 2), (5084, 7, 6), (5084, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_training_mean3 = np.mean(X3_train[:, :, 0])\n",
        "p_training_std3 = np.std(X3_train[:, :, 0])\n",
        "\n",
        "temp_training_mean3 = np.mean(X3_train[:, :, 1])\n",
        "temp_training_std3 = np.std(X3_train[:, :, 1])\n",
        "\n",
        "def preprocess3(X):\n",
        "  X[:, :, 0] = (X[:, :, 0] - p_training_mean3) / p_training_std3\n",
        "  X[:, :, 1] = (X[:, :, 1] - temp_training_mean3) / temp_training_std3\n",
        "  return X\n",
        "\n",
        "def preprocess_output(y):\n",
        "  y[:, 0] = (y[:, 0] - p_training_mean3) / p_training_std3\n",
        "  y[:, 1] = (y[:, 1] - temp_training_mean3) / temp_training_std3\n",
        "  return y"
      ],
      "metadata": {
        "id": "L-s47c3wKH0v"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess3(X3_train)\n",
        "preprocess3(X3_val)\n",
        "preprocess3(X3_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Cux-t5EKKJe",
        "outputId": "a86018af-2de6-43ef-a5cc-00d0d2260754"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-1.07355644e+00,  1.04009390e+00, -9.65925826e-01,\n",
              "          2.58819045e-01,  5.22877889e-01, -8.52407598e-01],\n",
              "        [-1.00807435e+00,  9.90728707e-01, -8.66025404e-01,\n",
              "          5.00000000e-01,  5.22266764e-01, -8.52782169e-01],\n",
              "        [-9.51080673e-01,  8.49685305e-01, -7.07106781e-01,\n",
              "          7.07106781e-01,  5.21655371e-01, -8.53156301e-01],\n",
              "        ...,\n",
              "        [-8.80748054e-01,  6.66328883e-01, -2.58819045e-01,\n",
              "          9.65925826e-01,  5.20431780e-01, -8.53903251e-01],\n",
              "        [-8.29817537e-01,  5.37039098e-01, -7.16474371e-12,\n",
              "          1.00000000e+00,  5.19819584e-01, -8.54276068e-01],\n",
              "        [-8.01927015e-01,  4.74744929e-01,  2.58819045e-01,\n",
              "          9.65925826e-01,  5.19207120e-01, -8.54648446e-01]],\n",
              "\n",
              "       [[-1.00807435e+00,  9.90728707e-01, -8.66025404e-01,\n",
              "          5.00000000e-01,  5.22266764e-01, -8.52782169e-01],\n",
              "        [-9.51080673e-01,  8.49685305e-01, -7.07106781e-01,\n",
              "          7.07106781e-01,  5.21655371e-01, -8.53156301e-01],\n",
              "        [-9.14701732e-01,  7.55656370e-01, -5.00000000e-01,\n",
              "          8.66025404e-01,  5.21043709e-01, -8.53529995e-01],\n",
              "        ...,\n",
              "        [-8.29817537e-01,  5.37039098e-01, -7.16474371e-12,\n",
              "          1.00000000e+00,  5.19819584e-01, -8.54276068e-01],\n",
              "        [-8.01927015e-01,  4.74744929e-01,  2.58819045e-01,\n",
              "          9.65925826e-01,  5.19207120e-01, -8.54648446e-01],\n",
              "        [-7.93438596e-01,  4.12450760e-01,  5.00000000e-01,\n",
              "          8.66025404e-01,  5.18594390e-01, -8.55020385e-01]],\n",
              "\n",
              "       [[-9.51080673e-01,  8.49685305e-01, -7.07106781e-01,\n",
              "          7.07106781e-01,  5.21655371e-01, -8.53156301e-01],\n",
              "        [-9.14701732e-01,  7.55656370e-01, -5.00000000e-01,\n",
              "          8.66025404e-01,  5.21043709e-01, -8.53529995e-01],\n",
              "        [-8.80748054e-01,  6.66328883e-01, -2.58819045e-01,\n",
              "          9.65925826e-01,  5.20431780e-01, -8.53903251e-01],\n",
              "        ...,\n",
              "        [-8.01927015e-01,  4.74744929e-01,  2.58819045e-01,\n",
              "          9.65925826e-01,  5.19207120e-01, -8.54648446e-01],\n",
              "        [-7.93438596e-01,  4.12450760e-01,  5.00000000e-01,\n",
              "          8.66025404e-01,  5.18594390e-01, -8.55020385e-01],\n",
              "        [-7.63122812e-01,  3.53682676e-01,  7.07106781e-01,\n",
              "          7.07106781e-01,  5.17981393e-01, -8.55391885e-01]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 1.73125990e+00, -6.19516796e-01, -5.37299608e-01,\n",
              "         -8.43391446e-01,  3.31630706e-03,  9.99994501e-01],\n",
              "        [ 1.71185780e+00, -5.23137138e-01, -7.37277337e-01,\n",
              "         -6.75590208e-01,  4.03308458e-03,  9.99991867e-01],\n",
              "        [ 1.64516307e+00, -8.20503643e-01, -8.87010833e-01,\n",
              "         -4.61748613e-01,  4.74986003e-03,  9.99988719e-01],\n",
              "        ...,\n",
              "        [ 1.61242203e+00, -1.15665708e+00, -9.99048222e-01,\n",
              "          4.36193874e-02,  6.18340325e-03,  9.99980883e-01],\n",
              "        [ 1.60150834e+00, -1.22247734e+00, -9.53716951e-01,\n",
              "          3.00705799e-01,  6.90017027e-03,  9.99976194e-01],\n",
              "        [ 1.50692310e+00, -1.27184253e+00, -8.43391446e-01,\n",
              "          5.37299608e-01,  7.61693375e-03,  9.99970991e-01]],\n",
              "\n",
              "       [[ 1.71185780e+00, -5.23137138e-01, -7.37277337e-01,\n",
              "         -6.75590208e-01,  4.03308458e-03,  9.99991867e-01],\n",
              "        [ 1.64516307e+00, -8.20503643e-01, -8.87010833e-01,\n",
              "         -4.61748613e-01,  4.74986003e-03,  9.99988719e-01],\n",
              "        [ 1.63182413e+00, -9.55670237e-01, -9.76296007e-01,\n",
              "         -2.16439614e-01,  5.46663304e-03,  9.99985058e-01],\n",
              "        ...,\n",
              "        [ 1.60150834e+00, -1.22247734e+00, -9.53716951e-01,\n",
              "          3.00705799e-01,  6.90017027e-03,  9.99976194e-01],\n",
              "        [ 1.50692310e+00, -1.27184253e+00, -8.43391446e-01,\n",
              "          5.37299608e-01,  7.61693375e-03,  9.99970991e-01],\n",
              "        [ 1.48145784e+00, -1.43051636e+00, -6.75590208e-01,\n",
              "          7.37277337e-01,  8.33369332e-03,  9.99965274e-01]],\n",
              "\n",
              "       [[ 1.64516307e+00, -8.20503643e-01, -8.87010833e-01,\n",
              "         -4.61748613e-01,  4.74986003e-03,  9.99988719e-01],\n",
              "        [ 1.63182413e+00, -9.55670237e-01, -9.76296007e-01,\n",
              "         -2.16439614e-01,  5.46663304e-03,  9.99985058e-01],\n",
              "        [ 1.61242203e+00, -1.15665708e+00, -9.99048222e-01,\n",
              "          4.36193874e-02,  6.18340325e-03,  9.99980883e-01],\n",
              "        ...,\n",
              "        [ 1.50692310e+00, -1.27184253e+00, -8.43391446e-01,\n",
              "          5.37299608e-01,  7.61693375e-03,  9.99970991e-01],\n",
              "        [ 1.48145784e+00, -1.43051636e+00, -6.75590208e-01,\n",
              "          7.37277337e-01,  8.33369332e-03,  9.99965274e-01],\n",
              "        [ 1.41597574e+00, -1.44697142e+00, -4.61748613e-01,\n",
              "          8.87010833e-01,  9.05044860e-03,  9.99959044e-01]]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess_output(y3_train)\n",
        "preprocess_output(y3_val)\n",
        "preprocess_output(y3_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFg1eAh8KpCS",
        "outputId": "19627d5d-e4af-440b-8a29-f34e6122ffaf"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.7934386 ,  0.41245076],\n",
              "       [-0.76312281,  0.35368268],\n",
              "       [-0.72189335,  0.32312327],\n",
              "       ...,\n",
              "       [ 1.48145784, -1.43051636],\n",
              "       [ 1.41597574, -1.44697142],\n",
              "       [ 1.35049365, -1.56920903]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 5: LSTM extended to use p (mbar) to predict temperature and p (mbar)"
      ],
      "metadata": {
        "id": "5QyFWzpEMFVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model5 = Sequential()\n",
        "model5.add(InputLayer((7, 6))) # 1 more input and the time window is increased by 1\n",
        "model5.add(LSTM(64))\n",
        "model5.add(Dense(8, 'relu'))\n",
        "model5.add(Dense(2, 'linear')) # Two outputs as we predict both temp and p\n",
        "\n",
        "model5.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bppE6_0SKqwf",
        "outputId": "03f254b8-b81a-4f83-925d-6093192e9730"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 64)                18176     \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 2)                 18        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18714 (73.10 KB)\n",
            "Trainable params: 18714 (73.10 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp5 = ModelCheckpoint('model5/', save_best_only=True)\n",
        "model5.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "qnBtNuZbKtLq"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model5.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bI9vi1deKvDa",
        "outputId": "e873110c-96c0-439f-f838-ec1be8bfb173"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 13s 6ms/step - loss: 0.1436 - root_mean_squared_error: 0.3789 - val_loss: 0.0319 - val_root_mean_squared_error: 0.1785\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0234 - root_mean_squared_error: 0.1529 - val_loss: 0.0156 - val_root_mean_squared_error: 0.1250\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0120 - root_mean_squared_error: 0.1096 - val_loss: 0.0079 - val_root_mean_squared_error: 0.0888\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0077 - root_mean_squared_error: 0.0875 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0743\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0062 - root_mean_squared_error: 0.0789 - val_loss: 0.0046 - val_root_mean_squared_error: 0.0677\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0055 - root_mean_squared_error: 0.0745 - val_loss: 0.0041 - val_root_mean_squared_error: 0.0639\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0052 - root_mean_squared_error: 0.0723 - val_loss: 0.0039 - val_root_mean_squared_error: 0.0628\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0051 - root_mean_squared_error: 0.0711 - val_loss: 0.0038 - val_root_mean_squared_error: 0.0613\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0050 - root_mean_squared_error: 0.0704 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0049 - root_mean_squared_error: 0.0700 - val_loss: 0.0037 - val_root_mean_squared_error: 0.0608\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799f2449a920>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions2(model, X, y, start=0, end=100):\n",
        "  predictions = model.predict(X)\n",
        "  p_preds, temp_preds = predictions[:, 0], predictions[:, 1]\n",
        "  p_actuals, temp_actuals = y[:, 0], y[:, 1]\n",
        "  df = pd.DataFrame(data={'Temperature Predictions': temp_preds,\n",
        "                          'Temperature Actuals':temp_actuals,\n",
        "                          'Pressure Predictions': p_preds,\n",
        "                          'Pressure Actuals': p_actuals\n",
        "                          })\n",
        "  plt.plot(df['Temperature Predictions'][start:end])\n",
        "  plt.plot(df['Temperature Actuals'][start:end])\n",
        "  plt.plot(df['Pressure Predictions'][start:end])\n",
        "  plt.plot(df['Pressure Actuals'][start:end])\n",
        "  return df"
      ],
      "metadata": {
        "id": "OkNulpkDKxeo"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions2(model5, X3_test, y3_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "id": "X5YII_-cK0EE",
        "outputId": "04d2371f-d453-486a-e277-0bf8199de5ec"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature Predictions  Temperature Actuals  Pressure Predictions  \\\n",
              "0                    0.413623             0.412451             -0.783256   \n",
              "1                    0.370521             0.353683             -0.769543   \n",
              "2                    0.332580             0.323123             -0.735520   \n",
              "3                    0.337727             0.250251             -0.685811   \n",
              "4                    0.288688             0.254952             -0.601203   \n",
              "...                       ...                  ...                   ...   \n",
              "5079                -1.270231            -1.222477              1.599388   \n",
              "5080                -1.283506            -1.271843              1.582804   \n",
              "5081                -1.293181            -1.430516              1.490766   \n",
              "5082                -1.472723            -1.446971              1.442107   \n",
              "5083                -1.463069            -1.569209              1.364853   \n",
              "\n",
              "      Pressure Actuals  \n",
              "0            -0.793439  \n",
              "1            -0.763123  \n",
              "2            -0.721893  \n",
              "3            -0.652773  \n",
              "4            -0.652773  \n",
              "...                ...  \n",
              "5079          1.601508  \n",
              "5080          1.506923  \n",
              "5081          1.481458  \n",
              "5082          1.415976  \n",
              "5083          1.350494  \n",
              "\n",
              "[5084 rows x 4 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature Predictions</th>\n",
              "      <th>Temperature Actuals</th>\n",
              "      <th>Pressure Predictions</th>\n",
              "      <th>Pressure Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.413623</td>\n",
              "      <td>0.412451</td>\n",
              "      <td>-0.783256</td>\n",
              "      <td>-0.793439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.370521</td>\n",
              "      <td>0.353683</td>\n",
              "      <td>-0.769543</td>\n",
              "      <td>-0.763123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.332580</td>\n",
              "      <td>0.323123</td>\n",
              "      <td>-0.735520</td>\n",
              "      <td>-0.721893</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0.250251</td>\n",
              "      <td>-0.685811</td>\n",
              "      <td>-0.652773</td>\n",
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              "      <td>-0.652773</td>\n",
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              "      <th>5080</th>\n",
              "      <td>-1.283506</td>\n",
              "      <td>-1.271843</td>\n",
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              "      <th>5081</th>\n",
              "      <td>-1.293181</td>\n",
              "      <td>-1.430516</td>\n",
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              "      <td>1.481458</td>\n",
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              "      <th>5082</th>\n",
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              "      <td>1.415976</td>\n",
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              "      <th>5083</th>\n",
              "      <td>-1.463069</td>\n",
              "      <td>-1.569209</td>\n",
              "      <td>1.364853</td>\n",
              "      <td>1.350494</td>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dae487f4-3ad6-4914-b7a6-eab54c975eda button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dae487f4-3ad6-4914-b7a6-eab54c975eda');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ce0c3c4c-24e2-4286-b5aa-2c4e8790a95a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce0c3c4c-24e2-4286-b5aa-2c4e8790a95a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce0c3c4c-24e2-4286-b5aa-2c4e8790a95a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def postprocess_temp(arr):\n",
        "  arr = (arr*temp_training_std3) + temp_training_mean3\n",
        "  return arr\n",
        "\n",
        "def postprocess_p(arr):\n",
        "  arr = (arr*p_training_std3) + p_training_mean3\n",
        "  return arr"
      ],
      "metadata": {
        "id": "OWIf8TU9K4FM"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_predictions_postprocessed(model, X, y):\n",
        "  predictions = model.predict(X)\n",
        "  p_preds, temp_preds = postprocess_p(predictions[:, 0]), postprocess_temp(predictions[:, 1])\n",
        "  p_actuals, temp_actuals = postprocess_p(y[:, 0]), postprocess_temp(y[:, 1])\n",
        "  df = pd.DataFrame(data={'Temperature Predictions': temp_preds,\n",
        "                          'Temperature Actuals':temp_actuals,\n",
        "                          'Pressure Predictions': p_preds,\n",
        "                          'Pressure Actuals': p_actuals\n",
        "                          })\n",
        "  return df"
      ],
      "metadata": {
        "id": "8NOodExgK5ug"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "post_processed_df = get_predictions_postprocessed(model5, X3_test, y3_test)\n",
        "post_processed_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "P6aHeOq1K7M2",
        "outputId": "98c6fdf1-5baf-4584-ee5d-b1437689fd9f"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature Predictions  Temperature Actuals  Pressure Predictions  \\\n",
              "0                   12.939972                12.93            982.513977   \n",
              "1                   12.573258                12.43            982.627075   \n",
              "2                   12.250460                12.17            982.907654   \n",
              "3                   12.294250                11.55            983.317566   \n",
              "4                   11.877028                11.59            984.015320   \n",
              "...                       ...                  ...                   ...   \n",
              "5079                -1.386287                -0.98           1002.162537   \n",
              "5080                -1.499233                -1.40           1002.025757   \n",
              "5081                -1.581552                -2.75           1001.266785   \n",
              "5082                -3.109091                -2.89           1000.865540   \n",
              "5083                -3.026958                -3.93           1000.228455   \n",
              "\n",
              "      Pressure Actuals  \n",
              "0               982.43  \n",
              "1               982.68  \n",
              "2               983.02  \n",
              "3               983.59  \n",
              "4               983.59  \n",
              "...                ...  \n",
              "5079           1002.18  \n",
              "5080           1001.40  \n",
              "5081           1001.19  \n",
              "5082           1000.65  \n",
              "5083           1000.11  \n",
              "\n",
              "[5084 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature Predictions</th>\n",
              "      <th>Temperature Actuals</th>\n",
              "      <th>Pressure Predictions</th>\n",
              "      <th>Pressure Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.939972</td>\n",
              "      <td>12.93</td>\n",
              "      <td>982.513977</td>\n",
              "      <td>982.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.573258</td>\n",
              "      <td>12.43</td>\n",
              "      <td>982.627075</td>\n",
              "      <td>982.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.250460</td>\n",
              "      <td>12.17</td>\n",
              "      <td>982.907654</td>\n",
              "      <td>983.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.294250</td>\n",
              "      <td>11.55</td>\n",
              "      <td>983.317566</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.877028</td>\n",
              "      <td>11.59</td>\n",
              "      <td>984.015320</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5079</th>\n",
              "      <td>-1.386287</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>1002.162537</td>\n",
              "      <td>1002.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5080</th>\n",
              "      <td>-1.499233</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>1002.025757</td>\n",
              "      <td>1001.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>-1.581552</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>1001.266785</td>\n",
              "      <td>1001.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-3.109091</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>1000.865540</td>\n",
              "      <td>1000.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-3.026958</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>1000.228455</td>\n",
              "      <td>1000.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5084 rows × 4 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f314e04e-09f2-44d7-a17c-21e6abfcf987 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f314e04e-09f2-44d7-a17c-21e6abfcf987');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a97aedde-3250-4158-bbc1-ffdfe6bbf314\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a97aedde-3250-4158-bbc1-ffdfe6bbf314')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a97aedde-3250-4158-bbc1-ffdfe6bbf314 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start, end = 0, 100\n",
        "plt.plot(post_processed_df['Temperature Predictions'][start:end])\n",
        "plt.plot(post_processed_df['Temperature Actuals'][start:end])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "kp9S16gFK9TH",
        "outputId": "95dac573-95ca-4e5a-8f62-e725dbab799b"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x799f49f74580>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Temperature\n",
        "mse(post_processed_df['Temperature Predictions'], post_processed_df['Temperature Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-YZ6lSbPEhA",
        "outputId": "34306dd2-1a52-4d29-e9e6-c869c1ea6f78"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.47150319273322605"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So model 5 is the current best model compared to the previous best model 4 with MSE 0.48279482668783036"
      ],
      "metadata": {
        "id": "uJQeoq5uP4BF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(post_processed_df['Pressure Predictions'][start:end])\n",
        "plt.plot(post_processed_df['Pressure Actuals'][start:end])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "39tTqOqjK_K4",
        "outputId": "12e8f5b3-665d-4ee9-8e2f-4c781882e79a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x799f241fe680>]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Pressure\n",
        "mse(post_processed_df['Pressure Predictions'], post_processed_df['Pressure Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3J8FNxuKPF9e",
        "outputId": "0cdba66b-1dcd-4821-9a77-70fb03862eb1"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08772302050821382"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 6: Multiple LSTM blocks"
      ],
      "metadata": {
        "id": "a06xXsYnOMYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model6 = Sequential()\n",
        "model6.add(InputLayer((7, 6)))\n",
        "model6.add(LSTM(32, return_sequences=True))\n",
        "model6.add(LSTM(16))\n",
        "model6.add(Dense(8, 'relu'))\n",
        "model6.add(Dense(2, 'linear'))\n",
        "\n",
        "model6.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPqmJQLBLA9G",
        "outputId": "177523bc-923e-4556-c54f-08bcece1a1dd"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_3 (LSTM)               (None, 7, 32)             4992      \n",
            "                                                                 \n",
            " lstm_4 (LSTM)               (None, 16)                3136      \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 8)                 136       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 2)                 18        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8282 (32.35 KB)\n",
            "Trainable params: 8282 (32.35 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp6 = ModelCheckpoint('model6/', save_best_only=True)\n",
        "model6.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "ZTxdD2QjLDA_"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model6.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0w_TCaYLEiC",
        "outputId": "b82b9ec0-4478-46b5-fefe-f3716fc12df8"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 21s 9ms/step - loss: 0.3430 - root_mean_squared_error: 0.5856 - val_loss: 0.0773 - val_root_mean_squared_error: 0.2780\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 19s 10ms/step - loss: 0.0554 - root_mean_squared_error: 0.2354 - val_loss: 0.0378 - val_root_mean_squared_error: 0.1945\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 18s 10ms/step - loss: 0.0300 - root_mean_squared_error: 0.1731 - val_loss: 0.0215 - val_root_mean_squared_error: 0.1468\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0180 - root_mean_squared_error: 0.1342 - val_loss: 0.0135 - val_root_mean_squared_error: 0.1162\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0131 - root_mean_squared_error: 0.1143 - val_loss: 0.0101 - val_root_mean_squared_error: 0.1004\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0105 - root_mean_squared_error: 0.1027 - val_loss: 0.0082 - val_root_mean_squared_error: 0.0907\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0089 - root_mean_squared_error: 0.0941 - val_loss: 0.0069 - val_root_mean_squared_error: 0.0831\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0076 - root_mean_squared_error: 0.0872 - val_loss: 0.0061 - val_root_mean_squared_error: 0.0779\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0067 - root_mean_squared_error: 0.0818 - val_loss: 0.0051 - val_root_mean_squared_error: 0.0711\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.0060 - root_mean_squared_error: 0.0777 - val_loss: 0.0048 - val_root_mean_squared_error: 0.0689\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799f24344df0>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post_processed_df = get_predictions_postprocessed(model6, X3_test, y3_test)\n",
        "post_processed_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Y5pMJE0VQJ5K",
        "outputId": "e1a3b591-7095-42ae-f9d5-8879af7d73d1"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature Predictions  Temperature Actuals  Pressure Predictions  \\\n",
              "0                   12.803729                12.93            982.664673   \n",
              "1                   12.499604                12.43            982.758911   \n",
              "2                   12.230174                12.17            982.966980   \n",
              "3                   12.229797                11.55            983.311768   \n",
              "4                   11.825236                11.59            983.971802   \n",
              "...                       ...                  ...                   ...   \n",
              "5079                -1.276131                -0.98           1002.122375   \n",
              "5080                -1.848392                -1.40           1001.770264   \n",
              "5081                -1.951209                -2.75           1001.080811   \n",
              "5082                -2.835655                -2.89           1000.649414   \n",
              "5083                -3.119873                -3.93           1000.121460   \n",
              "\n",
              "      Pressure Actuals  \n",
              "0               982.43  \n",
              "1               982.68  \n",
              "2               983.02  \n",
              "3               983.59  \n",
              "4               983.59  \n",
              "...                ...  \n",
              "5079           1002.18  \n",
              "5080           1001.40  \n",
              "5081           1001.19  \n",
              "5082           1000.65  \n",
              "5083           1000.11  \n",
              "\n",
              "[5084 rows x 4 columns]"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature Predictions</th>\n",
              "      <th>Temperature Actuals</th>\n",
              "      <th>Pressure Predictions</th>\n",
              "      <th>Pressure Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.803729</td>\n",
              "      <td>12.93</td>\n",
              "      <td>982.664673</td>\n",
              "      <td>982.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.499604</td>\n",
              "      <td>12.43</td>\n",
              "      <td>982.758911</td>\n",
              "      <td>982.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.230174</td>\n",
              "      <td>12.17</td>\n",
              "      <td>982.966980</td>\n",
              "      <td>983.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.229797</td>\n",
              "      <td>11.55</td>\n",
              "      <td>983.311768</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.825236</td>\n",
              "      <td>11.59</td>\n",
              "      <td>983.971802</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5079</th>\n",
              "      <td>-1.276131</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>1002.122375</td>\n",
              "      <td>1002.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5080</th>\n",
              "      <td>-1.848392</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>1001.770264</td>\n",
              "      <td>1001.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>-1.951209</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>1001.080811</td>\n",
              "      <td>1001.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-2.835655</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>1000.649414</td>\n",
              "      <td>1000.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-3.119873</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>1000.121460</td>\n",
              "      <td>1000.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5084 rows × 4 columns</p>\n",
              "</div>\n",
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              "        document.querySelector('#df-cfb2b38c-2dbf-4a52-a5c6-23c48e8525a5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-cfb2b38c-2dbf-4a52-a5c6-23c48e8525a5');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8870aa31-446d-49bd-be07-786caf6a2146 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Temperature\n",
        "mse(post_processed_df['Temperature Predictions'], post_processed_df['Temperature Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhRJfdh7QU5J",
        "outputId": "3c29fd5c-92e9-470b-8080-e9d5383f4c7e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5472266036794103"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Pressure\n",
        "mse(post_processed_df['Pressure Predictions'], post_processed_df['Pressure Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2apQEmHaQX91",
        "outputId": "cfb27b3c-d503-4c24-8d08-7055ca80ee06"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15274029699478192"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 6 is not better than model 5."
      ],
      "metadata": {
        "id": "56-EtyxEQekn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 7: CNN with the updated data"
      ],
      "metadata": {
        "id": "hErpwg4SPiac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model7 = Sequential()\n",
        "model7.add(InputLayer((7, 6)))\n",
        "model7.add(Conv1D(64, kernel_size=2, activation='relu'))\n",
        "model7.add(Flatten())\n",
        "model7.add(Dense(8, 'relu'))\n",
        "model7.add(Dense(2, 'linear'))\n",
        "model7.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkVgYhaZLGV-",
        "outputId": "b2abccbd-7a64-4796-bb82-77a44fdfb805"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_1 (Conv1D)           (None, 6, 64)             832       \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 384)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 8)                 3080      \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 2)                 18        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3930 (15.35 KB)\n",
            "Trainable params: 3930 (15.35 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp7 = ModelCheckpoint('model6/', save_best_only=True)\n",
        "model7.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "7-T2JCQ6LIY7"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model7.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp7])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cARXuIxNLKCO",
        "outputId": "0c8089d5-0e23-442f-d474-edd91e03f1a8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 9s 4ms/step - loss: 0.1173 - root_mean_squared_error: 0.3425 - val_loss: 0.0242 - val_root_mean_squared_error: 0.1556\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0164 - root_mean_squared_error: 0.1280 - val_loss: 0.0109 - val_root_mean_squared_error: 0.1042\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0098 - root_mean_squared_error: 0.0989 - val_loss: 0.0076 - val_root_mean_squared_error: 0.0871\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0078 - root_mean_squared_error: 0.0882 - val_loss: 0.0064 - val_root_mean_squared_error: 0.0803\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0068 - root_mean_squared_error: 0.0827 - val_loss: 0.0055 - val_root_mean_squared_error: 0.0739\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0063 - root_mean_squared_error: 0.0795 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0708\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0060 - root_mean_squared_error: 0.0774 - val_loss: 0.0052 - val_root_mean_squared_error: 0.0723\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0057 - root_mean_squared_error: 0.0758 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0056 - root_mean_squared_error: 0.0748 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0055 - root_mean_squared_error: 0.0740 - val_loss: 0.0042 - val_root_mean_squared_error: 0.0652\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799f3c95a500>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post_processed_df = get_predictions_postprocessed(model7, X3_test, y3_test)\n",
        "post_processed_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "zCDJm1mJQkL4",
        "outputId": "14e9b04c-6a6f-4065-ab72-847deb5fed1c"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature Predictions  Temperature Actuals  Pressure Predictions  \\\n",
              "0                   13.013609                12.93            982.266724   \n",
              "1                   12.604647                12.43            982.516479   \n",
              "2                   12.144541                12.17            982.849854   \n",
              "3                   12.141153                11.55            983.302979   \n",
              "4                   11.713078                11.59            983.747009   \n",
              "...                       ...                  ...                   ...   \n",
              "5079                -1.402145                -0.98           1002.074524   \n",
              "5080                -1.667769                -1.40           1001.715942   \n",
              "5081                -1.929741                -2.75           1000.996155   \n",
              "5082                -3.291414                -2.89           1000.416870   \n",
              "5083                -3.527141                -3.93           1000.101074   \n",
              "\n",
              "      Pressure Actuals  \n",
              "0               982.43  \n",
              "1               982.68  \n",
              "2               983.02  \n",
              "3               983.59  \n",
              "4               983.59  \n",
              "...                ...  \n",
              "5079           1002.18  \n",
              "5080           1001.40  \n",
              "5081           1001.19  \n",
              "5082           1000.65  \n",
              "5083           1000.11  \n",
              "\n",
              "[5084 rows x 4 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature Predictions</th>\n",
              "      <th>Temperature Actuals</th>\n",
              "      <th>Pressure Predictions</th>\n",
              "      <th>Pressure Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.013609</td>\n",
              "      <td>12.93</td>\n",
              "      <td>982.266724</td>\n",
              "      <td>982.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.604647</td>\n",
              "      <td>12.43</td>\n",
              "      <td>982.516479</td>\n",
              "      <td>982.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.144541</td>\n",
              "      <td>12.17</td>\n",
              "      <td>982.849854</td>\n",
              "      <td>983.02</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>12.141153</td>\n",
              "      <td>11.55</td>\n",
              "      <td>983.302979</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.713078</td>\n",
              "      <td>11.59</td>\n",
              "      <td>983.747009</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5079</th>\n",
              "      <td>-1.402145</td>\n",
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              "      <td>1002.074524</td>\n",
              "      <td>1002.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5080</th>\n",
              "      <td>-1.667769</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>1001.715942</td>\n",
              "      <td>1001.40</td>\n",
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              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>-1.929741</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>1000.996155</td>\n",
              "      <td>1001.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-3.291414</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>1000.416870</td>\n",
              "      <td>1000.65</td>\n",
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              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-3.527141</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>1000.101074</td>\n",
              "      <td>1000.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5084 rows × 4 columns</p>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df280a26-c3b3-4fb5-be14-2a228022ccea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Temperature\n",
        "mse(post_processed_df['Temperature Predictions'], post_processed_df['Temperature Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8Ahx3-oQlwd",
        "outputId": "96fcac00-027b-4f17-9a9e-8ea5f23e8093"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5083016221537751"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE Pressure\n",
        "mse(post_processed_df['Pressure Predictions'], post_processed_df['Pressure Actuals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOm-5XcaQol-",
        "outputId": "eb23a565-1ced-43a8-d7b3-80436fe87e2a"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11400654125446617"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 5 is still the best model."
      ],
      "metadata": {
        "id": "_3UiWPPAQjnV"
      }
    }
  ]
}