LSTM_Time_Series_Forecasting.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xinjie-h/lapin/blob/master/LSTM_Time_Series_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbrFD1vMR_qS"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yp96lCZTP1E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1q73lN27SDPC",
        "outputId": "e1f0eac0-188c-4d21-8b46-81a2999b2cbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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)"
      ],
      "execution_count": null,
      "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",
            "13574144/13568290 [==============================] - 0s 0us/step\n",
            "13582336/13568290 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "3SjxeNqoP4II"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IQKLKCu-P4eg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "RRVN-4QOSKAx",
        "outputId": "7fe7489b-43dc-41c8-fe7c-6f57d7942a91"
      },
      "source": [
        "df = pd.read_csv(csv_path)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>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",
              "      <td>0.22</td>\n",
              "      <td>1.94</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1307.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1.75</td>\n",
              "      <td>152.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01.01.2009 00:20:00</td>\n",
              "      <td>996.57</td>\n",
              "      <td>-8.41</td>\n",
              "      <td>265.01</td>\n",
              "      <td>-9.28</td>\n",
              "      <td>93.40</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01.01.2009 00:30:00</td>\n",
              "      <td>996.53</td>\n",
              "      <td>-8.51</td>\n",
              "      <td>264.91</td>\n",
              "      <td>-9.31</td>\n",
              "      <td>93.90</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2009 00:40:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.31</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.07</td>\n",
              "      <td>94.20</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2009 00:50:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.27</td>\n",
              "      <td>265.15</td>\n",
              "      <td>-9.04</td>\n",
              "      <td>94.10</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.08</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.09</td>\n",
              "      <td>1309.00</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.63</td>\n",
              "      <td>214.3</td>\n",
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              "    <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>420546</th>\n",
              "      <td>31.12.2016 23:20:00</td>\n",
              "      <td>1000.07</td>\n",
              "      <td>-4.05</td>\n",
              "      <td>269.10</td>\n",
              "      <td>-8.13</td>\n",
              "      <td>73.10</td>\n",
              "      <td>4.52</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.06</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1292.98</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.52</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420547</th>\n",
              "      <td>31.12.2016 23:30:00</td>\n",
              "      <td>999.93</td>\n",
              "      <td>-3.35</td>\n",
              "      <td>269.81</td>\n",
              "      <td>-8.06</td>\n",
              "      <td>69.71</td>\n",
              "      <td>4.77</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.07</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1289.44</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.92</td>\n",
              "      <td>234.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420548</th>\n",
              "      <td>31.12.2016 23:40:00</td>\n",
              "      <td>999.82</td>\n",
              "      <td>-3.16</td>\n",
              "      <td>270.01</td>\n",
              "      <td>-8.21</td>\n",
              "      <td>67.91</td>\n",
              "      <td>4.84</td>\n",
              "      <td>3.28</td>\n",
              "      <td>1.55</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.28</td>\n",
              "      <td>1288.39</td>\n",
              "      <td>1.08</td>\n",
              "      <td>2.00</td>\n",
              "      <td>215.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420549</th>\n",
              "      <td>31.12.2016 23:50:00</td>\n",
              "      <td>999.81</td>\n",
              "      <td>-4.23</td>\n",
              "      <td>268.94</td>\n",
              "      <td>-8.53</td>\n",
              "      <td>71.80</td>\n",
              "      <td>4.46</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.99</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1293.56</td>\n",
              "      <td>1.49</td>\n",
              "      <td>2.16</td>\n",
              "      <td>225.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420550</th>\n",
              "      <td>01.01.2017 00:00:00</td>\n",
              "      <td>999.82</td>\n",
              "      <td>-4.82</td>\n",
              "      <td>268.36</td>\n",
              "      <td>-8.42</td>\n",
              "      <td>75.70</td>\n",
              "      <td>4.27</td>\n",
              "      <td>3.23</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.01</td>\n",
              "      <td>3.23</td>\n",
              "      <td>1296.38</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.96</td>\n",
              "      <td>184.9</td>\n",
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              "<p>420551 rows × 15 columns</p>\n",
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            ],
            "text/plain": [
              "                  Date Time  p (mbar)  ...  max. wv (m/s)  wd (deg)\n",
              "0       01.01.2009 00:10:00    996.52  ...           1.75     152.3\n",
              "1       01.01.2009 00:20:00    996.57  ...           1.50     136.1\n",
              "2       01.01.2009 00:30:00    996.53  ...           0.63     171.6\n",
              "3       01.01.2009 00:40:00    996.51  ...           0.50     198.0\n",
              "4       01.01.2009 00:50:00    996.51  ...           0.63     214.3\n",
              "...                     ...       ...  ...            ...       ...\n",
              "420546  31.12.2016 23:20:00   1000.07  ...           1.52     240.0\n",
              "420547  31.12.2016 23:30:00    999.93  ...           1.92     234.3\n",
              "420548  31.12.2016 23:40:00    999.82  ...           2.00     215.2\n",
              "420549  31.12.2016 23:50:00    999.81  ...           2.16     225.8\n",
              "420550  01.01.2017 00:00:00    999.82  ...           1.96     184.9\n",
              "\n",
              "[420551 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
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          "height": 602
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        "id": "FDJTiftYSPGj",
        "outputId": "f157f680-baef-4443-ff0f-92803bff3ca6"
      },
      "source": [
        "df = df[5::6]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70091 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Date Time  p (mbar)  ...  max. wv (m/s)  wd (deg)\n",
              "5       01.01.2009 01:00:00    996.50  ...           0.63     192.7\n",
              "11      01.01.2009 02:00:00    996.62  ...           0.63     190.3\n",
              "17      01.01.2009 03:00:00    996.84  ...           0.63     167.2\n",
              "23      01.01.2009 04:00:00    996.99  ...           0.38     240.0\n",
              "29      01.01.2009 05:00:00    997.46  ...           0.88     157.0\n",
              "...                     ...       ...  ...            ...       ...\n",
              "420521  31.12.2016 19:10:00   1002.18  ...           1.36     190.6\n",
              "420527  31.12.2016 20:10:00   1001.40  ...           1.92     225.4\n",
              "420533  31.12.2016 21:10:00   1001.19  ...           1.56     158.7\n",
              "420539  31.12.2016 22:10:00   1000.65  ...           0.68     216.7\n",
              "420545  31.12.2016 23:10:00   1000.11  ...           1.00     202.6\n",
              "\n",
              "[70091 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C1MZOKB3Sg0X",
        "outputId": "b8ae7e79-5237-49e2-9ece-d5714983768b"
      },
      "source": [
        "df.index = pd.to_datetime(df['Date Time'], format='%d.%m.%Y %H:%M:%S')\n",
        "df[:26]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>01.01.2009 01:00:00</td>\n",
              "      <td>996.50</td>\n",
              "      <td>-8.05</td>\n",
              "      <td>265.38</td>\n",
              "      <td>-8.78</td>\n",
              "      <td>94.4</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.14</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.96</td>\n",
              "      <td>3.15</td>\n",
              "      <td>1307.86</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.63</td>\n",
              "      <td>192.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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>93.2</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>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>93.5</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>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>92.6</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>2009-01-01 05:00:00</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>92.2</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>2009-01-01 06:00:00</th>\n",
              "      <td>01.01.2009 06:00:00</td>\n",
              "      <td>997.71</td>\n",
              "      <td>-9.67</td>\n",
              "      <td>263.66</td>\n",
              "      <td>-10.62</td>\n",
              "      <td>92.7</td>\n",
              "      <td>2.93</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.72</td>\n",
              "      <td>1317.71</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.50</td>\n",
              "      <td>146.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 07:00:00</th>\n",
              "      <td>01.01.2009 07:00:00</td>\n",
              "      <td>998.33</td>\n",
              "      <td>-9.17</td>\n",
              "      <td>264.12</td>\n",
              "      <td>-10.10</td>\n",
              "      <td>92.9</td>\n",
              "      <td>3.04</td>\n",
              "      <td>2.83</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.83</td>\n",
              "      <td>1315.98</td>\n",
              "      <td>2.08</td>\n",
              "      <td>2.88</td>\n",
              "      <td>348.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 08:00:00</th>\n",
              "      <td>01.01.2009 08:00:00</td>\n",
              "      <td>999.17</td>\n",
              "      <td>-8.10</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.05</td>\n",
              "      <td>92.8</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1311.65</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.25</td>\n",
              "      <td>213.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 09:00:00</th>\n",
              "      <td>01.01.2009 09:00:00</td>\n",
              "      <td>999.69</td>\n",
              "      <td>-7.66</td>\n",
              "      <td>265.52</td>\n",
              "      <td>-8.84</td>\n",
              "      <td>91.2</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.13</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.95</td>\n",
              "      <td>3.13</td>\n",
              "      <td>1310.14</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.63</td>\n",
              "      <td>202.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 10:00:00</th>\n",
              "      <td>01.01.2009 10:00:00</td>\n",
              "      <td>1000.27</td>\n",
              "      <td>-7.04</td>\n",
              "      <td>266.10</td>\n",
              "      <td>-8.17</td>\n",
              "      <td>91.6</td>\n",
              "      <td>3.60</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.29</td>\n",
              "      <td>1307.76</td>\n",
              "      <td>1.45</td>\n",
              "      <td>3.00</td>\n",
              "      <td>292.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 11:00:00</th>\n",
              "      <td>01.01.2009 11:00:00</td>\n",
              "      <td>1000.87</td>\n",
              "      <td>-7.41</td>\n",
              "      <td>265.68</td>\n",
              "      <td>-8.66</td>\n",
              "      <td>90.7</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.17</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.97</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1310.43</td>\n",
              "      <td>1.91</td>\n",
              "      <td>3.38</td>\n",
              "      <td>269.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 12:00:00</th>\n",
              "      <td>01.01.2009 12:00:00</td>\n",
              "      <td>1000.30</td>\n",
              "      <td>-6.87</td>\n",
              "      <td>266.27</td>\n",
              "      <td>-8.28</td>\n",
              "      <td>89.6</td>\n",
              "      <td>3.64</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.03</td>\n",
              "      <td>3.26</td>\n",
              "      <td>1306.98</td>\n",
              "      <td>1.84</td>\n",
              "      <td>2.63</td>\n",
              "      <td>184.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 13:00:00</th>\n",
              "      <td>01.01.2009 13:00:00</td>\n",
              "      <td>1000.03</td>\n",
              "      <td>-5.89</td>\n",
              "      <td>267.27</td>\n",
              "      <td>-7.46</td>\n",
              "      <td>88.6</td>\n",
              "      <td>3.93</td>\n",
              "      <td>3.48</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.48</td>\n",
              "      <td>1301.73</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.25</td>\n",
              "      <td>187.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 14:00:00</th>\n",
              "      <td>01.01.2009 14:00:00</td>\n",
              "      <td>999.81</td>\n",
              "      <td>-5.94</td>\n",
              "      <td>267.24</td>\n",
              "      <td>-7.43</td>\n",
              "      <td>89.1</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.49</td>\n",
              "      <td>1301.67</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.00</td>\n",
              "      <td>144.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 15:00:00</th>\n",
              "      <td>01.01.2009 15:00:00</td>\n",
              "      <td>999.88</td>\n",
              "      <td>-5.69</td>\n",
              "      <td>267.48</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>90.4</td>\n",
              "      <td>3.99</td>\n",
              "      <td>3.61</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.25</td>\n",
              "      <td>3.61</td>\n",
              "      <td>1300.51</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.88</td>\n",
              "      <td>134.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 16:00:00</th>\n",
              "      <td>01.01.2009 16:00:00</td>\n",
              "      <td>999.94</td>\n",
              "      <td>-5.40</td>\n",
              "      <td>267.76</td>\n",
              "      <td>-6.86</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.08</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.27</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1299.17</td>\n",
              "      <td>1.40</td>\n",
              "      <td>2.13</td>\n",
              "      <td>145.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 17:00:00</th>\n",
              "      <td>01.01.2009 17:00:00</td>\n",
              "      <td>1000.17</td>\n",
              "      <td>-5.37</td>\n",
              "      <td>267.78</td>\n",
              "      <td>-6.82</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.09</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.28</td>\n",
              "      <td>3.66</td>\n",
              "      <td>1299.27</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.63</td>\n",
              "      <td>142.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 18:00:00</th>\n",
              "      <td>01.01.2009 18:00:00</td>\n",
              "      <td>1000.16</td>\n",
              "      <td>-5.25</td>\n",
              "      <td>267.90</td>\n",
              "      <td>-6.75</td>\n",
              "      <td>89.1</td>\n",
              "      <td>4.13</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.29</td>\n",
              "      <td>3.68</td>\n",
              "      <td>1298.68</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.00</td>\n",
              "      <td>183.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 19:00:00</th>\n",
              "      <td>01.01.2009 19:00:00</td>\n",
              "      <td>1000.22</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>268.03</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>89.4</td>\n",
              "      <td>4.17</td>\n",
              "      <td>3.73</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2.32</td>\n",
              "      <td>3.73</td>\n",
              "      <td>1298.07</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.75</td>\n",
              "      <td>176.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 20:00:00</th>\n",
              "      <td>01.01.2009 20:00:00</td>\n",
              "      <td>1000.22</td>\n",
              "      <td>-4.90</td>\n",
              "      <td>268.24</td>\n",
              "      <td>-6.38</td>\n",
              "      <td>89.3</td>\n",
              "      <td>4.24</td>\n",
              "      <td>3.79</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.36</td>\n",
              "      <td>3.78</td>\n",
              "      <td>1297.05</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.13</td>\n",
              "      <td>195.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 21:00:00</th>\n",
              "      <td>01.01.2009 21:00:00</td>\n",
              "      <td>1000.19</td>\n",
              "      <td>-4.80</td>\n",
              "      <td>268.35</td>\n",
              "      <td>-6.14</td>\n",
              "      <td>90.2</td>\n",
              "      <td>4.27</td>\n",
              "      <td>3.85</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.40</td>\n",
              "      <td>3.85</td>\n",
              "      <td>1296.45</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.75</td>\n",
              "      <td>206.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 22:00:00</th>\n",
              "      <td>01.01.2009 22:00:00</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>-4.50</td>\n",
              "      <td>268.66</td>\n",
              "      <td>-5.82</td>\n",
              "      <td>90.4</td>\n",
              "      <td>4.37</td>\n",
              "      <td>3.95</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.95</td>\n",
              "      <td>1294.72</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.63</td>\n",
              "      <td>175.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 23:00:00</th>\n",
              "      <td>01.01.2009 23:00:00</td>\n",
              "      <td>999.77</td>\n",
              "      <td>-4.47</td>\n",
              "      <td>268.71</td>\n",
              "      <td>-5.84</td>\n",
              "      <td>90.1</td>\n",
              "      <td>4.38</td>\n",
              "      <td>3.95</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.95</td>\n",
              "      <td>1294.29</td>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "      <td>219.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-02 00:00:00</th>\n",
              "      <td>02.01.2009 00:00:00</td>\n",
              "      <td>999.59</td>\n",
              "      <td>-4.54</td>\n",
              "      <td>268.65</td>\n",
              "      <td>-5.46</td>\n",
              "      <td>93.2</td>\n",
              "      <td>4.36</td>\n",
              "      <td>4.06</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.53</td>\n",
              "      <td>4.06</td>\n",
              "      <td>1294.33</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.88</td>\n",
              "      <td>155.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-02 01:00:00</th>\n",
              "      <td>02.01.2009 01:00:00</td>\n",
              "      <td>999.34</td>\n",
              "      <td>-4.44</td>\n",
              "      <td>268.77</td>\n",
              "      <td>-5.19</td>\n",
              "      <td>94.4</td>\n",
              "      <td>4.39</td>\n",
              "      <td>4.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.58</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1293.47</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.00</td>\n",
              "      <td>148.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-02 02:00:00</th>\n",
              "      <td>02.01.2009 02:00:00</td>\n",
              "      <td>998.93</td>\n",
              "      <td>-4.29</td>\n",
              "      <td>268.95</td>\n",
              "      <td>-5.04</td>\n",
              "      <td>94.5</td>\n",
              "      <td>4.44</td>\n",
              "      <td>4.20</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.62</td>\n",
              "      <td>4.20</td>\n",
              "      <td>1292.22</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.88</td>\n",
              "      <td>209.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Date Time  p (mbar)  ...  max. wv (m/s)  wd (deg)\n",
              "Date Time                                           ...                         \n",
              "2009-01-01 01:00:00  01.01.2009 01:00:00    996.50  ...           0.63     192.7\n",
              "2009-01-01 02:00:00  01.01.2009 02:00:00    996.62  ...           0.63     190.3\n",
              "2009-01-01 03:00:00  01.01.2009 03:00:00    996.84  ...           0.63     167.2\n",
              "2009-01-01 04:00:00  01.01.2009 04:00:00    996.99  ...           0.38     240.0\n",
              "2009-01-01 05:00:00  01.01.2009 05:00:00    997.46  ...           0.88     157.0\n",
              "2009-01-01 06:00:00  01.01.2009 06:00:00    997.71  ...           0.50     146.0\n",
              "2009-01-01 07:00:00  01.01.2009 07:00:00    998.33  ...           2.88     348.8\n",
              "2009-01-01 08:00:00  01.01.2009 08:00:00    999.17  ...           1.25     213.9\n",
              "2009-01-01 09:00:00  01.01.2009 09:00:00    999.69  ...           0.63     202.2\n",
              "2009-01-01 10:00:00  01.01.2009 10:00:00   1000.27  ...           3.00     292.6\n",
              "2009-01-01 11:00:00  01.01.2009 11:00:00   1000.87  ...           3.38     269.6\n",
              "2009-01-01 12:00:00  01.01.2009 12:00:00   1000.30  ...           2.63     184.4\n",
              "2009-01-01 13:00:00  01.01.2009 13:00:00   1000.03  ...           1.25     187.3\n",
              "2009-01-01 14:00:00  01.01.2009 14:00:00    999.81  ...           2.00     144.0\n",
              "2009-01-01 15:00:00  01.01.2009 15:00:00    999.88  ...           1.88     134.9\n",
              "2009-01-01 16:00:00  01.01.2009 16:00:00    999.94  ...           2.13     145.5\n",
              "2009-01-01 17:00:00  01.01.2009 17:00:00   1000.17  ...           1.63     142.2\n",
              "2009-01-01 18:00:00  01.01.2009 18:00:00   1000.16  ...           1.00     183.7\n",
              "2009-01-01 19:00:00  01.01.2009 19:00:00   1000.22  ...           0.75     176.7\n",
              "2009-01-01 20:00:00  01.01.2009 20:00:00   1000.22  ...           1.13     195.2\n",
              "2009-01-01 21:00:00  01.01.2009 21:00:00   1000.19  ...           0.75     206.3\n",
              "2009-01-01 22:00:00  01.01.2009 22:00:00   1000.00  ...           0.63     175.7\n",
              "2009-01-01 23:00:00  01.01.2009 23:00:00    999.77  ...           1.00     219.3\n",
              "2009-01-02 00:00:00  02.01.2009 00:00:00    999.59  ...           0.88     155.0\n",
              "2009-01-02 01:00:00  02.01.2009 01:00:00    999.34  ...           1.00     148.1\n",
              "2009-01-02 02:00:00  02.01.2009 02:00:00    998.93  ...           0.88     209.8\n",
              "\n",
              "[26 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "3fWZ3nYxS3oe",
        "outputId": "39e85f19-f71c-471d-eed6-a1552cb0b769"
      },
      "source": [
        "temp = df['T (degC)']\n",
        "temp.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd179711790>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bY2yEu2QTBXP"
      },
      "source": [
        "# [[[1], [2], [3], [4], [5]]] [6]\n",
        "# [[[2], [3], [4], [5], [6]]] [7]\n",
        "# [[[3], [4], [5], [6], [7]]] [8]\n",
        "\n",
        "def df_to_X_y(df, window_size=5):\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+window_size]]\n",
        "    X.append(row)\n",
        "    label = df_as_np[i+window_size]\n",
        "    y.append(label)\n",
        "  return np.array(X), np.array(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhGUH0NoV9Zq",
        "outputId": "c18fba4f-40c7-4555-f5ae-32bcf916a5c2"
      },
      "source": [
        "WINDOW_SIZE = 5\n",
        "X1, y1 = df_to_X_y(temp, WINDOW_SIZE)\n",
        "X1.shape, y1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70086, 5, 1), (70086,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsy2-BjnWMhB",
        "outputId": "d0c731ff-68d6-4165-8f5c-43c5dc3b145b"
      },
      "source": [
        "X_train1, y_train1 = X1[:60000], y1[:60000]\n",
        "X_val1, y_val1 = X1[60000:65000], y1[60000:65000]\n",
        "X_test1, y_test1 = X1[65000:], y1[65000:]\n",
        "X_train1.shape, y_train1.shape, X_val1.shape, y_val1.shape, X_test1.shape, y_test1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 5, 1), (60000,), (5000, 5, 1), (5000,), (5086, 5, 1), (5086,))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jZz4ZjpW217",
        "outputId": "cd0012a7-a71d-48de-ae20-6b5ff97d37c9"
      },
      "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",
        "model1 = Sequential()\n",
        "model1.add(InputLayer((5, 1)))\n",
        "model1.add(LSTM(64))\n",
        "model1.add(Dense(8, 'relu'))\n",
        "model1.add(Dense(1, 'linear'))\n",
        "\n",
        "model1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 64)                16896     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 17,425\n",
            "Trainable params: 17,425\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jMK7auDXwEr"
      },
      "source": [
        "cp1 = ModelCheckpoint('model1/', save_best_only=True)\n",
        "model1.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWeSakSwYLtr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9977d223-2642-449a-db72-97a6f52bacdd"
      },
      "source": [
        "model1.fit(X_train1, y_train1, validation_data=(X_val1, y_val1), epochs=10, callbacks=[cp1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 10s 4ms/step - loss: 30.7511 - root_mean_squared_error: 5.5454 - val_loss: 1.8956 - val_root_mean_squared_error: 1.3768\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 3.3528 - root_mean_squared_error: 1.8311 - val_loss: 0.7869 - val_root_mean_squared_error: 0.8871\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 1.2615 - root_mean_squared_error: 1.1232 - val_loss: 0.5363 - val_root_mean_squared_error: 0.7323\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.8123 - root_mean_squared_error: 0.9013 - val_loss: 0.5114 - val_root_mean_squared_error: 0.7151\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.7138 - root_mean_squared_error: 0.8448 - val_loss: 0.5100 - val_root_mean_squared_error: 0.7141\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6830 - root_mean_squared_error: 0.8265 - val_loss: 0.5000 - val_root_mean_squared_error: 0.7071\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6676 - root_mean_squared_error: 0.8171 - val_loss: 0.4971 - val_root_mean_squared_error: 0.7051\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6609 - root_mean_squared_error: 0.8130 - val_loss: 0.5005 - val_root_mean_squared_error: 0.7075\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6545 - root_mean_squared_error: 0.8090 - val_loss: 0.4952 - val_root_mean_squared_error: 0.7037\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model1/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.6517 - root_mean_squared_error: 0.8073 - val_loss: 0.5070 - val_root_mean_squared_error: 0.7121\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd178596690>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdaqGHG4YZkN"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model1 = load_model('model1/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "byObmr8CZRhp",
        "outputId": "d11e1086-ae1f-414e-b9d2-f6412487f00c"
      },
      "source": [
        "train_predictions = model1.predict(X_train1).flatten()\n",
        "train_results = pd.DataFrame(data={'Train Predictions':train_predictions, 'Actuals':y_train1})\n",
        "train_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Train Predictions</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-9.864591</td>\n",
              "      <td>-9.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-9.750214</td>\n",
              "      <td>-9.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8.872981</td>\n",
              "      <td>-8.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-7.415159</td>\n",
              "      <td>-7.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-7.236970</td>\n",
              "      <td>-7.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59995</th>\n",
              "      <td>6.209857</td>\n",
              "      <td>6.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59996</th>\n",
              "      <td>7.322401</td>\n",
              "      <td>9.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59997</th>\n",
              "      <td>12.221851</td>\n",
              "      <td>13.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59998</th>\n",
              "      <td>16.154484</td>\n",
              "      <td>15.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59999</th>\n",
              "      <td>16.346542</td>\n",
              "      <td>15.54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>60000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Train Predictions  Actuals\n",
              "0              -9.864591    -9.67\n",
              "1              -9.750214    -9.17\n",
              "2              -8.872981    -8.10\n",
              "3              -7.415159    -7.66\n",
              "4              -7.236970    -7.04\n",
              "...                  ...      ...\n",
              "59995           6.209857     6.07\n",
              "59996           7.322401     9.88\n",
              "59997          12.221851    13.53\n",
              "59998          16.154484    15.43\n",
              "59999          16.346542    15.54\n",
              "\n",
              "[60000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "KTqY8r6_Zpev",
        "outputId": "012c15d4-b097-4eb5-dd73-10cd2e893780"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(train_results['Train Predictions'][50:100])\n",
        "plt.plot(train_results['Actuals'][50:100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1775d7550>]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "vuY5nmGYZ8ix",
        "outputId": "e3a633dc-5bd7-48b9-bb2c-32f9328465fb"
      },
      "source": [
        "val_predictions = model1.predict(X_val1).flatten()\n",
        "val_results = pd.DataFrame(data={'Val Predictions':val_predictions, 'Actuals':y_val1})\n",
        "val_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Val Predictions</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.266132</td>\n",
              "      <td>14.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.166448</td>\n",
              "      <td>13.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.927699</td>\n",
              "      <td>12.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.416977</td>\n",
              "      <td>11.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.273677</td>\n",
              "      <td>10.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>17.221283</td>\n",
              "      <td>18.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>17.241358</td>\n",
              "      <td>17.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>17.084192</td>\n",
              "      <td>16.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>15.701090</td>\n",
              "      <td>15.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>14.952646</td>\n",
              "      <td>15.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Val Predictions  Actuals\n",
              "0           15.266132    14.02\n",
              "1           13.166448    13.67\n",
              "2           12.927699    12.27\n",
              "3           11.416977    11.19\n",
              "4           10.273677    10.85\n",
              "...               ...      ...\n",
              "4995        17.221283    18.27\n",
              "4996        17.241358    17.85\n",
              "4997        17.084192    16.65\n",
              "4998        15.701090    15.85\n",
              "4999        14.952646    15.09\n",
              "\n",
              "[5000 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6MlctzuuaQww",
        "outputId": "bd97d664-5c63-494b-da21-1fbd4088431e"
      },
      "source": [
        "plt.plot(val_results['Val Predictions'][:100])\n",
        "plt.plot(val_results['Actuals'][:100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd176f02890>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "5O59Q8MTaRN7",
        "outputId": "7579be1d-97c0-46ca-c30c-cbb92b06c8fc"
      },
      "source": [
        "test_predictions = model1.predict(X_test1).flatten()\n",
        "test_results = pd.DataFrame(data={'Test Predictions':test_predictions, 'Actuals':y_test1})\n",
        "test_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test Predictions</th>\n",
              "      <th>Actuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.270700</td>\n",
              "      <td>13.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.134656</td>\n",
              "      <td>13.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.756431</td>\n",
              "      <td>12.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.389000</td>\n",
              "      <td>12.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.948853</td>\n",
              "      <td>12.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>-1.005016</td>\n",
              "      <td>-0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-1.346865</td>\n",
              "      <td>-1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-1.608510</td>\n",
              "      <td>-2.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5084</th>\n",
              "      <td>-3.483826</td>\n",
              "      <td>-2.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5085</th>\n",
              "      <td>-3.178735</td>\n",
              "      <td>-3.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5086 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Test Predictions  Actuals\n",
              "0            14.270700    13.99\n",
              "1            13.134656    13.46\n",
              "2            12.756431    12.93\n",
              "3            12.389000    12.43\n",
              "4            11.948853    12.17\n",
              "...                ...      ...\n",
              "5081         -1.005016    -0.98\n",
              "5082         -1.346865    -1.40\n",
              "5083         -1.608510    -2.75\n",
              "5084         -3.483826    -2.89\n",
              "5085         -3.178735    -3.93\n",
              "\n",
              "[5086 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8UzGIfEaW-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e593d54f-c02f-4da0-d2fe-fd3f95636618"
      },
      "source": [
        "plt.plot(test_results['Test Predictions'][:100])\n",
        "plt.plot(test_results['Actuals'][:100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd176d95d50>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWdZkoPo-MZM"
      },
      "source": [
        "# Split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OK63d14raZFZ"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "def plot_predictions1(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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "m2JjeH6V-uQn",
        "outputId": "fbf8c505-b1d3-4ca6-bbf1-d93e35d08509"
      },
      "source": [
        "plot_predictions1(model1, X_test1, y_test1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       14.270700    13.99\n",
              " 1       13.134656    13.46\n",
              " 2       12.756431    12.93\n",
              " 3       12.389000    12.43\n",
              " 4       11.948853    12.17\n",
              " ...           ...      ...\n",
              " 5081    -1.005016    -0.98\n",
              " 5082    -1.346865    -1.40\n",
              " 5083    -1.608510    -2.75\n",
              " 5084    -3.483826    -2.89\n",
              " 5085    -3.178735    -3.93\n",
              " \n",
              " [5086 rows x 2 columns], 0.6629716596824853)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Avi4Zydf-ynb",
        "outputId": "80184d81-832f-4451-cede-d744f577aa5f"
      },
      "source": [
        "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_1 (Conv1D)            (None, 4, 64)             192       \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 8)                 2056      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 2,257\n",
            "Trainable params: 2,257\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znQvX4Hf_0-X"
      },
      "source": [
        "cp2 = ModelCheckpoint('model2/', save_best_only=True)\n",
        "model2.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjSXAC_wAWaE"
      },
      "source": [
        "model2.fit(X_train1, y_train1, validation_data=(X_val1, y_val1), epochs=10, callbacks=[cp2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "sTXr6cN5Aae5",
        "outputId": "ef66b277-71a6-4e77-f324-9804d5f76092"
      },
      "source": [
        "plot_predictions1(model2, X_test1, y_test1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0       14.379455    13.99\n",
              " 1       13.320185    13.46\n",
              " 2       12.831345    12.93\n",
              " 3       12.474539    12.43\n",
              " 4       12.077660    12.17\n",
              " ...           ...      ...\n",
              " 5081    -1.261978    -0.98\n",
              " 5082    -1.631636    -1.40\n",
              " 5083    -1.727667    -2.75\n",
              " 5084    -3.041824    -2.89\n",
              " 5085    -3.277084    -3.93\n",
              " \n",
              " [5086 rows x 2 columns], 0.6780165389518743)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIKUNnb3A_x-",
        "outputId": "efe74678-1508-4336-f5a4-66ddf4bb86b9"
      },
      "source": [
        "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gru (GRU)                    (None, 64)                12864     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 13,393\n",
            "Trainable params: 13,393\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgcU2I5FBksr"
      },
      "source": [
        "cp3 = ModelCheckpoint('model3/', save_best_only=True)\n",
        "model3.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMcHF1rVB0Ry"
      },
      "source": [
        "model3.fit(X_train1, y_train1, validation_data=(X_val1, y_val1), epochs=10, callbacks=[cp3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kziIgnJgB2-F",
        "outputId": "1c464fd5-0f6e-44f2-95ba-1fd37f5f2c41"
      },
      "source": [
        "temp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date Time\n",
              "2009-01-01 01:00:00   -8.05\n",
              "2009-01-01 02:00:00   -8.88\n",
              "2009-01-01 03:00:00   -8.81\n",
              "2009-01-01 04:00:00   -9.05\n",
              "2009-01-01 05:00:00   -9.63\n",
              "                       ... \n",
              "2016-12-31 19:10:00   -0.98\n",
              "2016-12-31 20:10:00   -1.40\n",
              "2016-12-31 21:10:00   -2.75\n",
              "2016-12-31 22:10:00   -2.89\n",
              "2016-12-31 23:10:00   -3.93\n",
              "Name: T (degC), Length: 70091, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "LVV0xsEpHcYS",
        "outputId": "7630bd55-5dc4-419f-dd1e-33b7fdfbc04e"
      },
      "source": [
        "temp_df = pd.DataFrame({'Temperature':temp})\n",
        "temp_df['Seconds'] = temp_df.index.map(pd.Timestamp.timestamp)\n",
        "temp_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Seconds</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date Time</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>-8.05</td>\n",
              "      <td>1.230772e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\n",
              "      <td>-8.88</td>\n",
              "      <td>1.230775e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 03:00:00</th>\n",
              "      <td>-8.81</td>\n",
              "      <td>1.230779e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 04:00:00</th>\n",
              "      <td>-9.05</td>\n",
              "      <td>1.230782e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 05:00:00</th>\n",
              "      <td>-9.63</td>\n",
              "      <td>1.230786e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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>1.483211e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 20:10:00</th>\n",
              "      <td>-1.40</td>\n",
              "      <td>1.483215e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 21:10:00</th>\n",
              "      <td>-2.75</td>\n",
              "      <td>1.483219e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 22:10:00</th>\n",
              "      <td>-2.89</td>\n",
              "      <td>1.483222e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31 23:10:00</th>\n",
              "      <td>-3.93</td>\n",
              "      <td>1.483226e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70091 rows × 2 columns</p>\n",
              "</div>"
            ],
            "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",
              "\n",
              "[70091 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "trw64ao9GVsj",
        "outputId": "b206a050-021b-47aa-a3bd-0d1bdb468978"
      },
      "source": [
        "day = 24*60*60\n",
        "year = (365.2425)*day\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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Seconds</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>-8.05</td>\n",
              "      <td>1.230772e+09</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>-8.88</td>\n",
              "      <td>1.230775e+09</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>-8.81</td>\n",
              "      <td>1.230779e+09</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>1.230782e+09</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>-9.63</td>\n",
              "      <td>1.230786e+09</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>-0.98</td>\n",
              "      <td>1.483211e+09</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>1.483215e+09</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>1.483219e+09</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>1.483222e+09</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>1.483226e+09</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",
              "</div>"
            ],
            "text/plain": [
              "                     Temperature       Seconds  ...  Year sin  Year cos\n",
              "Date Time                                       ...                    \n",
              "2009-01-01 01:00:00        -8.05  1.230772e+09  ...  0.010049  0.999950\n",
              "2009-01-01 02:00:00        -8.88  1.230775e+09  ...  0.010766  0.999942\n",
              "2009-01-01 03:00:00        -8.81  1.230779e+09  ...  0.011483  0.999934\n",
              "2009-01-01 04:00:00        -9.05  1.230782e+09  ...  0.012199  0.999926\n",
              "2009-01-01 05:00:00        -9.63  1.230786e+09  ...  0.012916  0.999917\n",
              "...                          ...           ...  ...       ...       ...\n",
              "2016-12-31 19:10:00        -0.98  1.483211e+09  ...  0.006900  0.999976\n",
              "2016-12-31 20:10:00        -1.40  1.483215e+09  ...  0.007617  0.999971\n",
              "2016-12-31 21:10:00        -2.75  1.483219e+09  ...  0.008334  0.999965\n",
              "2016-12-31 22:10:00        -2.89  1.483222e+09  ...  0.009050  0.999959\n",
              "2016-12-31 23:10:00        -3.93  1.483226e+09  ...  0.009767  0.999952\n",
              "\n",
              "[70091 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "oHwTktN7CWHM",
        "outputId": "c758e91a-0b5e-4ce5-a101-af7081aa1462"
      },
      "source": [
        "temp_df = temp_df.drop('Seconds', axis=1)\n",
        "temp_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-01-01 02:00:00</th>\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>-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",
              "    </tr>\n",
              "    <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>"
            ],
            "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]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQSGTWaPL-E5"
      },
      "source": [
        "def df_to_X_y2(df, window_size=6):\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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItaFUqTsCyna",
        "outputId": "3af2900d-062a-4473-d33d-42f75c85e19e"
      },
      "source": [
        "X2, y2 = df_to_X_y2(temp_df)\n",
        "X2.shape, y2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70085, 6, 5), (70085,))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmtFJ_0pD-xh",
        "outputId": "8793bef1-9040-4a04-97fe-21d54bb5fd90"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 6, 5), (60000,), (5000, 6, 5), (5000,), (5085, 6, 5), (5085,))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a51kf2YbGlio"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLnCvqyfHPDs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aecbb65b-3d08-4e19-b95d-ec76de72c56a"
      },
      "source": [
        "preprocess(X2_train)\n",
        "preprocess(X2_val)\n",
        "preprocess(X2_test)"
      ],
      "execution_count": null,
      "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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AER68ty-DDK4",
        "outputId": "cb6ebf99-1b7c-417d-951c-78b83a247ebd"
      },
      "source": [
        "model4 = Sequential()\n",
        "model4.add(InputLayer((6, 5)))\n",
        "model4.add(LSTM(64))\n",
        "model4.add(Dense(8, 'relu'))\n",
        "model4.add(Dense(1, 'linear'))\n",
        "\n",
        "model4.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_2 (LSTM)                (None, 64)                17920     \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 18,449\n",
            "Trainable params: 18,449\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYWXv67bENSm"
      },
      "source": [
        "cp4 = ModelCheckpoint('model4/', save_best_only=True)\n",
        "model4.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nodJelV-EZRI"
      },
      "source": [
        "model4.fit(X2_train, y2_train, validation_data=(X2_val, y2_val), epochs=10, callbacks=[cp4])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "jAgqCN2XEhNs",
        "outputId": "719f3825-808d-493d-c686-ac0ec1a3b85f"
      },
      "source": [
        "plot_predictions1(model4, X2_test, y2_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(      Predictions  Actuals\n",
              " 0        1.270767    13.46\n",
              " 1        1.065866    12.93\n",
              " 2        0.886102    12.43\n",
              " 3        0.762327    12.17\n",
              " 4        0.687246    11.55\n",
              " ...           ...      ...\n",
              " 5080    -0.123489    -0.98\n",
              " 5081    -0.125271    -1.40\n",
              " 5082    -0.130911    -2.75\n",
              " 5083    -0.147739    -2.89\n",
              " 5084    -0.161792    -3.93\n",
              " \n",
              " [5085 rows x 2 columns], 184.3117044089246)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "evOTkLI4Phga",
        "outputId": "789806c9-2823-4ca5-ebed-65d021e8a00a"
      },
      "source": [
        "p_temp_df = pd.concat([df['p (mbar)'], temp_df], axis=1)\n",
        "p_temp_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>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",
              "</div>"
            ],
            "text/plain": [
              "                     p (mbar)  Temperature  ...  Year sin  Year cos\n",
              "Date Time                                   ...                    \n",
              "2009-01-01 01:00:00    996.50        -8.05  ...  0.010049  0.999950\n",
              "2009-01-01 02:00:00    996.62        -8.88  ...  0.010766  0.999942\n",
              "2009-01-01 03:00:00    996.84        -8.81  ...  0.011483  0.999934\n",
              "2009-01-01 04:00:00    996.99        -9.05  ...  0.012199  0.999926\n",
              "2009-01-01 05:00:00    997.46        -9.63  ...  0.012916  0.999917\n",
              "...                       ...          ...  ...       ...       ...\n",
              "2016-12-31 19:10:00   1002.18        -0.98  ...  0.006900  0.999976\n",
              "2016-12-31 20:10:00   1001.40        -1.40  ...  0.007617  0.999971\n",
              "2016-12-31 21:10:00   1001.19        -2.75  ...  0.008334  0.999965\n",
              "2016-12-31 22:10:00   1000.65        -2.89  ...  0.009050  0.999959\n",
              "2016-12-31 23:10:00   1000.11        -3.93  ...  0.009767  0.999952\n",
              "\n",
              "[70091 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCLh2-T8RgS4"
      },
      "source": [
        "def df_to_X_y3(df, window_size=7):\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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpuUPN1cSzdk",
        "outputId": "925f113a-190f-470b-dd66-b03170c5d887"
      },
      "source": [
        "X3, y3 = df_to_X_y3(p_temp_df)\n",
        "X3.shape, y3.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70084, 7, 6), (70084, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wzVmWCVUMYY",
        "outputId": "da570e15-02e9-40d7-8cda-f24c7929bcdc"
      },
      "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"
      ],
      "execution_count": null,
      "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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGbZvLocUY54"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YTj2X6CU_1J",
        "outputId": "aac36bbf-0390-4340-d12e-ababaeab1943"
      },
      "source": [
        "preprocess3(X3_train)\n",
        "preprocess3(X3_val)\n",
        "preprocess3(X3_test)"
      ],
      "execution_count": null,
      "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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKOm6mRLXgFZ",
        "outputId": "6dafb59f-9f0c-4382-f589-b7b2479c8685"
      },
      "source": [
        "preprocess_output(y3_train)\n",
        "preprocess_output(y3_val)\n",
        "preprocess_output(y3_test)"
      ],
      "execution_count": null,
      "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": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypEqobuNTg53",
        "outputId": "026cbebf-78d8-4810-fb7f-28830c770c67"
      },
      "source": [
        "model5 = Sequential()\n",
        "model5.add(InputLayer((7, 6)))\n",
        "model5.add(LSTM(64))\n",
        "model5.add(Dense(8, 'relu'))\n",
        "model5.add(Dense(2, 'linear'))\n",
        "\n",
        "model5.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_3 (LSTM)                (None, 64)                18176     \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 2)                 18        \n",
            "=================================================================\n",
            "Total params: 18,714\n",
            "Trainable params: 18,714\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q25hb7rXUCDI"
      },
      "source": [
        "cp5 = ModelCheckpoint('model5/', save_best_only=True)\n",
        "model5.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "t_xyuPiZUJ_n",
        "outputId": "94b1604f-9681-4c7d-ce22-c8ef05a3dc3b"
      },
      "source": [
        "model5.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            " 375/1875 [=====>........................] - ETA: 8s - loss: 0.0713 - root_mean_squared_error: 0.2670"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-6d1265bbe464>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX3_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my3_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX3_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my3_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcp5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1182\u001b[0m                 _r=1):\n\u001b[1;32m   1183\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3038\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3039\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3042\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1962\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1965\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1966\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi3EglPHZTeX"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "0i-aCfgVZC9L",
        "outputId": "fb268b8f-ce83-4251-84c5-5c3728135323"
      },
      "source": [
        "plot_predictions2(model5, X3_test, y3_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>0.497261</td>\n",
              "      <td>0.412451</td>\n",
              "      <td>-0.939157</td>\n",
              "      <td>-0.793439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.473668</td>\n",
              "      <td>0.353683</td>\n",
              "      <td>-0.892772</td>\n",
              "      <td>-0.763123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.458037</td>\n",
              "      <td>0.323123</td>\n",
              "      <td>-0.859892</td>\n",
              "      <td>-0.721893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.467024</td>\n",
              "      <td>0.250251</td>\n",
              "      <td>-0.840133</td>\n",
              "      <td>-0.652773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.488601</td>\n",
              "      <td>0.254952</td>\n",
              "      <td>-0.823547</td>\n",
              "      <td>-0.652773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5079</th>\n",
              "      <td>-0.917703</td>\n",
              "      <td>-1.222477</td>\n",
              "      <td>1.712903</td>\n",
              "      <td>1.601508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5080</th>\n",
              "      <td>-0.988455</td>\n",
              "      <td>-1.271843</td>\n",
              "      <td>1.652463</td>\n",
              "      <td>1.506923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>-1.087464</td>\n",
              "      <td>-1.430516</td>\n",
              "      <td>1.593468</td>\n",
              "      <td>1.481458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-1.219868</td>\n",
              "      <td>-1.446971</td>\n",
              "      <td>1.541024</td>\n",
              "      <td>1.415976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-1.350580</td>\n",
              "      <td>-1.569209</td>\n",
              "      <td>1.492155</td>\n",
              "      <td>1.350494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5084 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Temperature Predictions  ...  Pressure Actuals\n",
              "0                    0.497261  ...         -0.793439\n",
              "1                    0.473668  ...         -0.763123\n",
              "2                    0.458037  ...         -0.721893\n",
              "3                    0.467024  ...         -0.652773\n",
              "4                    0.488601  ...         -0.652773\n",
              "...                       ...  ...               ...\n",
              "5079                -0.917703  ...          1.601508\n",
              "5080                -0.988455  ...          1.506923\n",
              "5081                -1.087464  ...          1.481458\n",
              "5082                -1.219868  ...          1.415976\n",
              "5083                -1.350580  ...          1.350494\n",
              "\n",
              "[5084 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3biL9urbmdB"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aZSDhgjcbtI"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "dKO-Y5lCcT9P",
        "outputId": "8c401492-495a-4ae8-dc55-287307939f81"
      },
      "source": [
        "post_processed_df = get_predictions_postprocessed(model5, X3_test, y3_test)\n",
        "post_processed_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>13.651567</td>\n",
              "      <td>12.93</td>\n",
              "      <td>981.228333</td>\n",
              "      <td>982.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.450840</td>\n",
              "      <td>12.43</td>\n",
              "      <td>981.610901</td>\n",
              "      <td>982.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.317848</td>\n",
              "      <td>12.17</td>\n",
              "      <td>981.882019</td>\n",
              "      <td>983.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.394308</td>\n",
              "      <td>11.55</td>\n",
              "      <td>982.044983</td>\n",
              "      <td>983.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.577888</td>\n",
              "      <td>11.59</td>\n",
              "      <td>982.181763</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5079</th>\n",
              "      <td>1.613030</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>1003.098633</td>\n",
              "      <td>1002.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5080</th>\n",
              "      <td>1.011069</td>\n",
              "      <td>-1.40</td>\n",
              "      <td>1002.600220</td>\n",
              "      <td>1001.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5081</th>\n",
              "      <td>0.168693</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>1002.113708</td>\n",
              "      <td>1001.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5082</th>\n",
              "      <td>-0.957798</td>\n",
              "      <td>-2.89</td>\n",
              "      <td>1001.681274</td>\n",
              "      <td>1000.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5083</th>\n",
              "      <td>-2.069902</td>\n",
              "      <td>-3.93</td>\n",
              "      <td>1001.278259</td>\n",
              "      <td>1000.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5084 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Temperature Predictions  ...  Pressure Actuals\n",
              "0                   13.651567  ...            982.43\n",
              "1                   13.450840  ...            982.68\n",
              "2                   13.317848  ...            983.02\n",
              "3                   13.394308  ...            983.59\n",
              "4                   13.577888  ...            983.59\n",
              "...                       ...  ...               ...\n",
              "5079                 1.613030  ...           1002.18\n",
              "5080                 1.011069  ...           1001.40\n",
              "5081                 0.168693  ...           1001.19\n",
              "5082                -0.957798  ...           1000.65\n",
              "5083                -2.069902  ...           1000.11\n",
              "\n",
              "[5084 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7KIoImWHLv7O",
        "outputId": "e31a6d59-d68b-4c89-e2b3-6ddf4f7f53bb"
      },
      "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])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1642e5550>]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "w9qLd1KrL5kT",
        "outputId": "d74125b8-9859-4316-c7ff-a4112f45b370"
      },
      "source": [
        "plt.plot(post_processed_df['Pressure Predictions'][start:end])\n",
        "plt.plot(post_processed_df['Pressure Actuals'][start:end])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd16429a5d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkCeXZOMcuaV",
        "outputId": "b019c760-eb96-45da-dab6-03b0b86fc93e"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_4 (LSTM)                (None, 7, 32)             4992      \n",
            "_________________________________________________________________\n",
            "lstm_5 (LSTM)                (None, 16)                3136      \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 2)                 18        \n",
            "=================================================================\n",
            "Total params: 8,282\n",
            "Trainable params: 8,282\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0nbK--mdmAz"
      },
      "source": [
        "cp6 = ModelCheckpoint('model6/', save_best_only=True)\n",
        "model6.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "23zFBGVwefkH",
        "outputId": "feabcec9-0295-43ee-ac0a-e7f98e665483"
      },
      "source": [
        "model6.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            " 131/1875 [=>............................] - ETA: 12s - loss: 0.8951 - root_mean_squared_error: 0.9461"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-011f732ffe4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX3_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my3_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX3_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my3_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcp6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1182\u001b[0m                 _r=1):\n\u001b[1;32m   1183\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3038\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3039\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3042\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1962\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1965\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1966\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJ6iH7IReqyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "236b4ce2-71b5-4387-f875-52ebbfd405a3"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_3 (Conv1D)            (None, 6, 64)             832       \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 384)               0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 8)                 3080      \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 2)                 18        \n",
            "=================================================================\n",
            "Total params: 3,930\n",
            "Trainable params: 3,930\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Lu1RTuHTQIN"
      },
      "source": [
        "cp7 = ModelCheckpoint('model6/', save_best_only=True)\n",
        "model7.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3Tp1QAMTUcf",
        "outputId": "f2640996-b9a3-422b-b9c3-203b9ae82dfe"
      },
      "source": [
        "model7.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp7])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0984 - root_mean_squared_error: 0.3136 - val_loss: 0.0216 - val_root_mean_squared_error: 0.1471\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0143 - root_mean_squared_error: 0.1196 - val_loss: 0.0098 - val_root_mean_squared_error: 0.0988\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0092 - root_mean_squared_error: 0.0959 - val_loss: 0.0072 - val_root_mean_squared_error: 0.0849\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0076 - root_mean_squared_error: 0.0873 - val_loss: 0.0059 - val_root_mean_squared_error: 0.0770\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0069 - root_mean_squared_error: 0.0828 - val_loss: 0.0054 - val_root_mean_squared_error: 0.0734\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0064 - root_mean_squared_error: 0.0800 - val_loss: 0.0050 - val_root_mean_squared_error: 0.0710\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0061 - root_mean_squared_error: 0.0782 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0687\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0059 - root_mean_squared_error: 0.0768 - val_loss: 0.0047 - val_root_mean_squared_error: 0.0689\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0057 - root_mean_squared_error: 0.0757 - val_loss: 0.0045 - val_root_mean_squared_error: 0.0670\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0056 - root_mean_squared_error: 0.0749 - val_loss: 0.0044 - val_root_mean_squared_error: 0.0661\n",
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model6/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd16383a9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    }
  ]
}