main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Time Series Forecasting Practice"
      ],
      "metadata": {
        "id": "tnfFnC5zmQig"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Setup"
      ],
      "metadata": {
        "id": "G5x-5XTVm0-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "yrp3hXFtnCOL"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Check GPU"
      ],
      "metadata": {
        "id": "KTnlqK-6niGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi -L"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebTulbiUoWRz",
        "outputId": "4146f843-4beb-445d-b4fa-17807cc20619"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-2c9cde46-2f6c-3f3c-1537-d0b87bace2ca)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###The weather Dataset\n",
        "\n",
        "This tutorial uses a <a href=\"https://www.bgc-jena.mpg.de/wetter/\" class=\"external\">weather time series dataset</a> recorded by the <a href=\"https://www.bgc-jena.mpg.de\" class=\"external\">Max Planck Institute for Biogeochemistry</a>.\n",
        "\n",
        "This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016. This section of the dataset was prepared by François Chollet for his book <a href=\"https://www.manning.com/books/deep-learning-with-python\" class=\"external\">Deep Learning with Python</a>."
      ],
      "metadata": {
        "id": "24s87NclobWs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zip_path = tf.keras.utils.get_file(\n",
        "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
        "    fname='jena_climate_2009_2016.csv.zip',\n",
        "    extract=True)\n",
        "csv_path, _ = os.path.splitext(zip_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKss2sACpF8b",
        "outputId": "15a00cdb-729c-4ee5-bb5a-5a25bfe45fb6"
      },
      "execution_count": 3,
      "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": [
        "This tutorial will just deal with **hourly predictions**, so start by sub-sampling the data from 10-minute intervals to one-hour intervals:"
      ],
      "metadata": {
        "id": "sDBYvpBYqWFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(csv_path)\n",
        "# Slice [start:stop:step], starting from index 5 take every 6th record.\n",
        "df = df[5::6]\n",
        "\n",
        "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')"
      ],
      "metadata": {
        "id": "JIGGXphEqXDX"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Visualize"
      ],
      "metadata": {
        "id": "rcV8Jae_rtkE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a glance at the data. Here are the first few rows:"
      ],
      "metadata": {
        "id": "UWkEEfXlrx41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "pk3vwZfCr3he",
        "outputId": "1e207ee3-11f2-4ddd-bdea-015773308dfd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  VPmax (mbar)  \\\n",
              "5     996.50     -8.05    265.38        -8.78    94.4          3.33   \n",
              "11    996.62     -8.88    264.54        -9.77    93.2          3.12   \n",
              "17    996.84     -8.81    264.59        -9.66    93.5          3.13   \n",
              "23    996.99     -9.05    264.34       -10.02    92.6          3.07   \n",
              "29    997.46     -9.63    263.72       -10.65    92.2          2.94   \n",
              "\n",
              "    VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
              "5           3.14          0.19       1.96             3.15       1307.86   \n",
              "11          2.90          0.21       1.81             2.91       1312.25   \n",
              "17          2.93          0.20       1.83             2.94       1312.18   \n",
              "23          2.85          0.23       1.78             2.85       1313.61   \n",
              "29          2.71          0.23       1.69             2.71       1317.19   \n",
              "\n",
              "    wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "5       0.21           0.63     192.7  \n",
              "11      0.25           0.63     190.3  \n",
              "17      0.18           0.63     167.2  \n",
              "23      0.10           0.38     240.0  \n",
              "29      0.40           0.88     157.0  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\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>11</th>\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>17</th>\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>23</th>\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>29</th>\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",
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              "  </tbody>\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is the evolution of a few features over time:"
      ],
      "metadata": {
        "id": "_7px0vDLr4mG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mpl.rcParams['figure.figsize'] = (8, 6)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "\n",
        "plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n",
        "plot_features = df[plot_cols]\n",
        "plot_features.index = date_time\n",
        "_ = plot_features.plot(subplots=True)\n",
        "\n",
        "plot_features = df[plot_cols][:480]\n",
        "plot_features.index = date_time[:480]\n",
        "_ = plot_features.plot(subplots=True)"
      ],
      "metadata": {
        "colab": {
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          "height": 764
        },
        "id": "pGRNq8QFsBeg",
        "outputId": "e29d7706-6726-46f2-bddf-2823965079cb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Inspect and Cleanup"
      ],
      "metadata": {
        "id": "cS01jH0ZsF14"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, look at the statistics of the dataset:"
      ],
      "metadata": {
        "id": "CDekjomMs04Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "GY5PVQLGs4MV",
        "outputId": "8c475d84-9acc-4eac-8e05-869a725cdca0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count         mean        std      min      25%      50%  \\\n",
              "p (mbar)         70091.0   989.212842   8.358886   913.60   984.20   989.57   \n",
              "T (degC)         70091.0     9.450482   8.423384   -22.76     3.35     9.41   \n",
              "Tpot (K)         70091.0   283.493086   8.504424   250.85   277.44   283.46   \n",
              "Tdew (degC)      70091.0     4.956471   6.730081   -24.80     0.24     5.21   \n",
              "rh (%)           70091.0    76.009788  16.474920    13.88    65.21    79.30   \n",
              "VPmax (mbar)     70091.0    13.576576   7.739883     0.97     7.77    11.82   \n",
              "VPact (mbar)     70091.0     9.533968   4.183658     0.81     6.22     8.86   \n",
              "VPdef (mbar)     70091.0     4.042536   4.898549     0.00     0.87     2.19   \n",
              "sh (g/kg)        70091.0     6.022560   2.655812     0.51     3.92     5.59   \n",
              "H2OC (mmol/mol)  70091.0     9.640437   4.234862     0.81     6.29     8.96   \n",
              "rho (g/m**3)     70091.0  1216.061232  39.974263  1059.45  1187.47  1213.80   \n",
              "wv (m/s)         70091.0     1.702567  65.447512 -9999.00     0.99     1.76   \n",
              "max. wv (m/s)    70091.0     2.963041  75.597657 -9999.00     1.76     2.98   \n",
              "wd (deg)         70091.0   174.789095  86.619431     0.00   125.30   198.10   \n",
              "\n",
              "                      75%      max  \n",
              "p (mbar)          994.720  1015.29  \n",
              "T (degC)           15.480    37.28  \n",
              "Tpot (K)          289.530   311.21  \n",
              "Tdew (degC)        10.080    23.06  \n",
              "rh (%)             89.400   100.00  \n",
              "VPmax (mbar)       17.610    63.77  \n",
              "VPact (mbar)       12.360    28.25  \n",
              "VPdef (mbar)        5.300    46.01  \n",
              "sh (g/kg)           7.800    18.07  \n",
              "H2OC (mmol/mol)    12.490    28.74  \n",
              "rho (g/m**3)     1242.765  1393.54  \n",
              "wv (m/s)            2.860    14.01  \n",
              "max. wv (m/s)       4.740    23.50  \n",
              "wd (deg)          234.000   360.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1968df2-74cc-4f72-87e1-400606075834\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>p (mbar)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>989.212842</td>\n",
              "      <td>8.358886</td>\n",
              "      <td>913.60</td>\n",
              "      <td>984.20</td>\n",
              "      <td>989.57</td>\n",
              "      <td>994.720</td>\n",
              "      <td>1015.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T (degC)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>9.450482</td>\n",
              "      <td>8.423384</td>\n",
              "      <td>-22.76</td>\n",
              "      <td>3.35</td>\n",
              "      <td>9.41</td>\n",
              "      <td>15.480</td>\n",
              "      <td>37.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tpot (K)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>283.493086</td>\n",
              "      <td>8.504424</td>\n",
              "      <td>250.85</td>\n",
              "      <td>277.44</td>\n",
              "      <td>283.46</td>\n",
              "      <td>289.530</td>\n",
              "      <td>311.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>4.956471</td>\n",
              "      <td>6.730081</td>\n",
              "      <td>-24.80</td>\n",
              "      <td>0.24</td>\n",
              "      <td>5.21</td>\n",
              "      <td>10.080</td>\n",
              "      <td>23.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rh (%)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>76.009788</td>\n",
              "      <td>16.474920</td>\n",
              "      <td>13.88</td>\n",
              "      <td>65.21</td>\n",
              "      <td>79.30</td>\n",
              "      <td>89.400</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>13.576576</td>\n",
              "      <td>7.739883</td>\n",
              "      <td>0.97</td>\n",
              "      <td>7.77</td>\n",
              "      <td>11.82</td>\n",
              "      <td>17.610</td>\n",
              "      <td>63.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>9.533968</td>\n",
              "      <td>4.183658</td>\n",
              "      <td>0.81</td>\n",
              "      <td>6.22</td>\n",
              "      <td>8.86</td>\n",
              "      <td>12.360</td>\n",
              "      <td>28.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>4.042536</td>\n",
              "      <td>4.898549</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>2.19</td>\n",
              "      <td>5.300</td>\n",
              "      <td>46.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>6.022560</td>\n",
              "      <td>2.655812</td>\n",
              "      <td>0.51</td>\n",
              "      <td>3.92</td>\n",
              "      <td>5.59</td>\n",
              "      <td>7.800</td>\n",
              "      <td>18.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>9.640437</td>\n",
              "      <td>4.234862</td>\n",
              "      <td>0.81</td>\n",
              "      <td>6.29</td>\n",
              "      <td>8.96</td>\n",
              "      <td>12.490</td>\n",
              "      <td>28.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>1216.061232</td>\n",
              "      <td>39.974263</td>\n",
              "      <td>1059.45</td>\n",
              "      <td>1187.47</td>\n",
              "      <td>1213.80</td>\n",
              "      <td>1242.765</td>\n",
              "      <td>1393.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wv (m/s)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>1.702567</td>\n",
              "      <td>65.447512</td>\n",
              "      <td>-9999.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.860</td>\n",
              "      <td>14.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>2.963041</td>\n",
              "      <td>75.597657</td>\n",
              "      <td>-9999.00</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.98</td>\n",
              "      <td>4.740</td>\n",
              "      <td>23.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wd (deg)</th>\n",
              "      <td>70091.0</td>\n",
              "      <td>174.789095</td>\n",
              "      <td>86.619431</td>\n",
              "      <td>0.00</td>\n",
              "      <td>125.30</td>\n",
              "      <td>198.10</td>\n",
              "      <td>234.000</td>\n",
              "      <td>360.00</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Wind Velocity"
      ],
      "metadata": {
        "id": "y1fQ1oohs6yP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One thing that should stand out is the min value of the wind velocity (wv (m/s)) and the maximum value (max. wv (m/s)) columns. This -9999 is likely erroneous.\n",
        "\n",
        "There's a separate wind direction column, so the velocity should be greater than zero (>=0). Replace it with zeros:"
      ],
      "metadata": {
        "id": "deDCUNvbtC_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wv = df['wv (m/s)']\n",
        "bad_wv = wv == -9999.0\n",
        "wv[bad_wv] = 0.0\n",
        "\n",
        "max_wv = df['max. wv (m/s)']\n",
        "bad_max_wv = max_wv == -9999.0\n",
        "max_wv[bad_max_wv] = 0.0\n",
        "\n",
        "# The above inplace edits are reflected in the DataFrame.\n",
        "df['wv (m/s)'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owzaZIigtLCb",
        "outputId": "aebf46ca-9e69-4d01-cd06-27bd24338b4a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Split The Data"
      ],
      "metadata": {
        "id": "yrEg4w5vtk9-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You'll use a `(70%, 20%, 10%)` split for the training, validation, and test sets. Note the data is **not** being randomly shuffled before splitting. This is for two reasons:\n",
        "\n",
        "1. It ensures that chopping the data into windows of consecutive samples is still possible.\n",
        "2. It ensures that the validation/test results are more realistic, being evaluated on the data collected after the model was trained."
      ],
      "metadata": {
        "id": "Qxm_1dJ1uDrE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = len(df)\n",
        "train_df = df[0:int(n*0.7)]\n",
        "val_df = df[int(n*0.7):int(n*0.9)]\n",
        "test_df = df[int(n*0.9):]"
      ],
      "metadata": {
        "id": "E6C4Ad8OuJUL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Normalize the data"
      ],
      "metadata": {
        "id": "9EMqhWBHui76"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is important to scale features before training a neural network. Normalization is a common way of doing this scaling: subtract the mean and divide by the standard deviation of each feature.\n",
        "\n",
        "The mean and standard deviation should only be computed using the training data so that the models have no access to the values in the validation and test sets.\n",
        "\n",
        "It's also arguable that the model shouldn't have access to future values in the training set when training, and that this normalization should be done using moving averages. That's not the focus of this tutorial, and the validation and test sets ensure that you get (somewhat) honest metrics. So, in the interest of simplicity this tutorial uses a simple average."
      ],
      "metadata": {
        "id": "NBzrfP59uomZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_mean = train_df.mean()\n",
        "train_std = train_df.std()\n",
        "\n",
        "train_df = (train_df - train_mean) / train_std\n",
        "val_df = (val_df - train_mean) / train_std\n",
        "test_df = (test_df - train_mean) / train_std"
      ],
      "metadata": {
        "id": "Vs-RtqbYuwAq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, peek at the distribution of the features. Some features do have long tails, but there are no obvious errors like the `-9999` wind velocity value."
      ],
      "metadata": {
        "id": "lHLDfzChu72H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_std = (df - train_mean) / train_std\n",
        "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n",
        "plt.figure(figsize=(12, 6))\n",
        "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n",
        "_ = ax.set_xticklabels(df.keys(), rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "42V9pL7Pu8qu",
        "outputId": "41e8f606-ebce-432a-e4e7-18ee551bc1dd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PART I"
      ],
      "metadata": {
        "id": "W0mJSPiMv3i_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In PART I we will window the dataset without using `tf.keras.utils.timeseries_dataset_from_array`"
      ],
      "metadata": {
        "id": "hMoVS1edwDAD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Window the dataset\n"
      ],
      "metadata": {
        "id": "oCoUTj7Jv8tr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HORIZON = 1 # predict 1 step at a time\n",
        "WINDOW_SIZE = 3 # use 3 timesteps to predict the horizon"
      ],
      "metadata": {
        "id": "zImjp6o7wzQQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we'll write a function to take in an array and turn it into a window and horizon."
      ],
      "metadata": {
        "id": "cWjQx1GoxLKh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create function to label windowed data\n",
        "def get_labelled_windows(x, horizon=1):\n",
        "  \"\"\"\n",
        "  Creates labels for windowed dataset.\n",
        "\n",
        "  E.g. if horizon=1 (default)\n",
        "  Input: [1, 2, 3, 4, 5, 6] -> Output: ([1, 2, 3, 4, 5], [6])\n",
        "  \"\"\"\n",
        "  return x[:, :-horizon], x[:, -horizon:]"
      ],
      "metadata": {
        "id": "1dL5IIpsxS25"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out the window labelling function\n",
        "test_window, test_label = get_labelled_windows(tf.expand_dims(tf.range(8)+1, axis=0), horizon=HORIZON)\n",
        "print(f\"Window: {tf.squeeze(test_window).numpy()} -> Label: {tf.squeeze(test_label).numpy()}\")"
      ],
      "metadata": {
        "id": "Z4ImVT2nxX6k",
        "outputId": "dc81df5d-f172-4271-8fda-0b4e59c648fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [1 2 3 4 5 6 7] -> Label: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Utilize Numpy indexing to create windowed arrays"
      ],
      "metadata": {
        "id": "svRzfiOSxdPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create function to view NumPy arrays as windows \n",
        "def make_windows(x, window_size=7, horizon=1):\n",
        "  \"\"\"\n",
        "  Turns a 1D array into a 2D array of sequential windows of window_size.\n",
        "  \"\"\"\n",
        "  # 1. Create a window of specific window_size (add the horizon on the end for later labelling)\n",
        "  window_step = np.expand_dims(np.arange(window_size+horizon), axis=0)\n",
        "  # print(f\"Window step:\\n {window_step}\")\n",
        "\n",
        "  # 2. Create a 2D array of multiple window steps (minus 1 to account for 0 indexing)\n",
        "  window_indexes = window_step + np.expand_dims(np.arange(len(x)-(window_size+horizon-1)), axis=0).T # create 2D array of windows of size window_size\n",
        "  # print(f\"Window indexes:\\n {window_indexes[:3], window_indexes[-3:], window_indexes.shape}\")\n",
        "\n",
        "  # 3. Index on the target array (time series) with 2D array of multiple window steps\n",
        "  windowed_array = x[window_indexes]\n",
        "\n",
        "  # 4. Get the labelled windows\n",
        "  windows, labels = get_labelled_windows(windowed_array, horizon=horizon)\n",
        "\n",
        "  return windows, labels"
      ],
      "metadata": {
        "id": "_evPDMAdxx1X"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**Create train, val, test windows and labels**"
      ],
      "metadata": {
        "id": "l4GJus0ByHU7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this tutorial we will only be forecasting the temperature"
      ],
      "metadata": {
        "id": "vDJko8_Fx4Kr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Train windowed & label data**"
      ],
      "metadata": {
        "id": "qD5ixLnmy2U3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_temp_arr = train_df['T (degC)'].to_numpy()\n",
        "\n",
        "train_windows, train_labels = make_windows(train_temp_arr, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(train_windows), len(train_labels)\n",
        "\n"
      ],
      "metadata": {
        "id": "vRaXCKgqzz1x",
        "outputId": "a97657a4-9413-482b-d513-40fd439f2d6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49060, 49060)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the first 3 windows/labels\n",
        "for i in range(3):\n",
        "  print(f\"Window: {train_windows[i]} -> Label: {train_labels[i]}\")"
      ],
      "metadata": {
        "id": "87cMa2ya0Ldg",
        "outputId": "41f8f72f-a877-494e-d6b5-7a7c1d98534c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [-1.98247323 -2.07837211 -2.07028426] -> Label: [-2.09801405]\n",
            "Window: [-2.07837211 -2.07028426 -2.09801405] -> Label: [-2.16502773]\n",
            "Window: [-2.07028426 -2.09801405 -2.16502773] -> Label: [-2.16964936]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the last 3 windows/labels\n",
        "for i in range(3):\n",
        "  print(f\"Window: {train_windows[i-3]} -> Label: {train_labels[i-3]}\")"
      ],
      "metadata": {
        "id": "-UDNE-pR0PJo",
        "outputId": "0f606ea3-26f8-420d-fce8-e87ed233c136",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [0.93724325 0.90489182 0.87947284] -> Label: [0.74082386]\n",
            "Window: [0.90489182 0.87947284 0.74082386] -> Label: [0.71078325]\n",
            "Window: [0.87947284 0.74082386 0.71078325] -> Label: [0.65301284]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Validation windowed & label data**"
      ],
      "metadata": {
        "id": "_3wTtd340TWf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "val_temp_arr = val_df['T (degC)'].to_numpy()\n",
        "\n",
        "val_windows, val_labels = make_windows(val_temp_arr, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(val_windows), len(val_labels)"
      ],
      "metadata": {
        "id": "dQvEP-3g0cA5",
        "outputId": "050a3288-22b0-4901-cb86-cded89cf9dc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14015, 14015)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Test windowed & label data**"
      ],
      "metadata": {
        "id": "Epg2Dxnp02Fn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_temp_arr = test_df['T (degC)'].to_numpy()\n",
        "\n",
        "test_windows, test_labels = make_windows(test_temp_arr, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(test_windows), len(test_labels)"
      ],
      "metadata": {
        "id": "YKiX6N5f00JC",
        "outputId": "952b59ac-d0e9-4698-eae4-70d8dea97291",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7007, 7007)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Naive Forecast (baseline)"
      ],
      "metadata": {
        "id": "Hu59jGPjwCRH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As usual, let's start with a baseline.\n",
        "\n",
        "One of the most common baseline models for time series forecasting, the naïve model (also called the naïve forecast), requires no training at all.\n",
        "\n",
        "That's because all the naïve model does is use the previous timestep value to predict the next timestep value."
      ],
      "metadata": {
        "id": "9kD6nWfL1c2Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "naive_forecast = train_temp_arr[:-1]\n",
        "naive_forecast[:10], naive_forecast[-10:] # View frist 10 and last 10"
      ],
      "metadata": {
        "id": "7va5DxvIwyhZ",
        "outputId": "123e0015-76c0-499f-d28a-6928bf77a913",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-1.98247323, -2.07837211, -2.07028426, -2.09801405, -2.16502773,\n",
              "        -2.16964936, -2.11187895, -1.98825027, -1.93741231, -1.86577701]),\n",
              " array([1.58196102, 1.69403561, 1.66168418, 1.05509488, 0.97999335,\n",
              "        0.93724325, 0.90489182, 0.87947284, 0.74082386, 0.71078325]))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Plot Naive Forecast"
      ],
      "metadata": {
        "id": "0HXCE2YC1h57"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Create a plotting function"
      ],
      "metadata": {
        "id": "9VPHf7CP1skB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a function to plot time series data\n",
        "def plot_time_series(timesteps, values, format='.', start=0, end=None, label=None):\n",
        "  \"\"\"\n",
        "  Plots a timesteps (a series of points in time) against values (a series of values across timesteps).\n",
        "  \n",
        "  Parameters\n",
        "  ---------\n",
        "  timesteps : array of timesteps\n",
        "  values : array of values across time\n",
        "  format : style of plot, default \".\"\n",
        "  start : where to start the plot (setting a value will index from start of timesteps & values)\n",
        "  end : where to end the plot (setting a value will index from end of timesteps & values)\n",
        "  label : label to show on plot of values\n",
        "  \"\"\"\n",
        "  # Plot the series\n",
        "  plt.plot(timesteps[start:end], values[start:end], format, label=label)\n",
        "  plt.xlabel(\"Time\")\n",
        "  plt.ylabel(\"T (degC)\")\n",
        "  if label:\n",
        "    plt.legend(fontsize=14) # make label bigger\n",
        "  plt.grid(True)"
      ],
      "metadata": {
        "id": "ddVObCjvM9kG"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "#Plot the last 300 timesteps\n",
        "\n",
        "plot_time_series(timesteps=date_time[:len(train_temp_arr.tolist())], \n",
        "                 values=train_temp_arr, \n",
        "                 start=-300, \n",
        "                 label=\"Train Data\")\n",
        "\n",
        "plot_time_series(timesteps=date_time[:len(naive_forecast.tolist())+1], \n",
        "                 values=naive_forecast, \n",
        "                 start=-300,\n",
        "                 format='-' ,\n",
        "                 label=\"Naive Data\")\n",
        "\n",
        "len(naive_forecast.tolist()), len(train_temp_arr.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UxaoQgOHOKV3",
        "outputId": "2d1e3782-7379-4fd4-e6ef-3a5dba037d5c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49062, 49063)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we zoom in we see the naïve forecast comes slightly after the test data. This makes sense because the naive forecast uses the previous timestep value to predict the next timestep value.\n",
        "\n",
        "Forecast made. Time to evaluate it."
      ],
      "metadata": {
        "id": "zH-AcjIWO6lm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Evaluate Naive Forecast Model"
      ],
      "metadata": {
        "id": "oPUfhoDXQ8Dp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Metric Functions"
      ],
      "metadata": {
        "id": "KKc3_H1AtfSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# MASE implemented courtesy of sktime - https://github.com/alan-turing-institute/sktime/blob/ee7a06843a44f4aaec7582d847e36073a9ab0566/sktime/performance_metrics/forecasting/_functions.py#L16\n",
        "def mean_absolute_scaled_error(y_true, y_pred):\n",
        "  \"\"\"\n",
        "  Implement MASE (assuming no seasonality of data).\n",
        "  \"\"\"\n",
        "  mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n",
        "\n",
        "  # Find MAE of naive forecast (no seasonality)\n",
        "  mae_naive_no_season = tf.reduce_mean(tf.abs(y_true[1:] - y_true[:-1])) # our seasonality is 1 day (hence the shifting of 1 day)\n",
        "\n",
        "  return mae / mae_naive_no_season"
      ],
      "metadata": {
        "id": "brJqPvhhRbHB"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_preds(y_true, y_pred):\n",
        "  # Make sure float32 (for metric calculations)\n",
        "  y_true = tf.cast(y_true, dtype=tf.float32)\n",
        "  y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
        "\n",
        "  # Calculate various metrics\n",
        "  mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)\n",
        "  mse = tf.keras.metrics.mean_squared_error(y_true, y_pred) # puts and emphasis on outliers (all errors get squared)\n",
        "  rmse = tf.sqrt(mse)\n",
        "  mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)\n",
        "  mase = mean_absolute_scaled_error(y_true, y_pred)\n",
        "  \n",
        "  return {\"mae\": mae.numpy(),\n",
        "          \"mse\": mse.numpy(),\n",
        "          \"rmse\": rmse.numpy(),\n",
        "          \"mape\": mape.numpy(),\n",
        "          \"mase\": mase.numpy()}"
      ],
      "metadata": {
        "id": "ecBwwQKQRk6V"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "naive_results = evaluate_preds(y_true=train_temp_arr[1:],\n",
        "                               y_pred=naive_forecast)\n",
        "naive_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "We2YdoSwRrLj",
        "outputId": "b002c2fa-d6e2-4d70-e38c-06a4bc5dd35f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 0.08293154,\n",
              " 'mape': 64.16384,\n",
              " 'mase': 1.0000031,\n",
              " 'mse': 0.013971615,\n",
              " 'rmse': 0.11820158}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alright, looks like we've got some baselines to beat."
      ],
      "metadata": {
        "id": "v7_Myp0PRw9v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Model 0"
      ],
      "metadata": {
        "id": "vfX0lW7WSJGS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Create Early stopping callback"
      ],
      "metadata": {
        "id": "Wu8I7y2BSMfD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                                                  patience=2,\n",
        "                                                  mode='auto')"
      ],
      "metadata": {
        "id": "g70s8PKqSUJ9"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Create model"
      ],
      "metadata": {
        "id": "II2GdzkeSW9E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "model_0 = tf.keras.Sequential([\n",
        "  layers.Dense(32, activation='relu'),\n",
        "  layers.Dense(32, activation='relu'),\n",
        "  layers.Dense(HORIZON)\n",
        "])\n",
        "\n",
        "model_0.compile(loss='mae',\n",
        "                optimizer=tf.optimizers.Adam(),\n",
        "                metrics=['mae'])"
      ],
      "metadata": {
        "id": "pLDdvDj8SfYB"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Fit the model"
      ],
      "metadata": {
        "id": "UArZCZURSjC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_0 = model_0.fit(x=train_windows,\n",
        "                      y=train_labels,\n",
        "                      epochs=100,\n",
        "                      verbose=1,\n",
        "                      batch_size=128,\n",
        "                      validation_data=(val_windows, val_labels),\n",
        "                      callbacks=[early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3taOZwhhSvjQ",
        "outputId": "c52fe8b8-ef1d-4d9e-8371-4d8f90293a7a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "384/384 [==============================] - 7s 5ms/step - loss: 0.1196 - mae: 0.1196 - val_loss: 0.0601 - val_mae: 0.0601\n",
            "Epoch 2/100\n",
            "384/384 [==============================] - 1s 3ms/step - loss: 0.0631 - mae: 0.0631 - val_loss: 0.0599 - val_mae: 0.0599\n",
            "Epoch 3/100\n",
            "384/384 [==============================] - 1s 3ms/step - loss: 0.0628 - mae: 0.0628 - val_loss: 0.0621 - val_mae: 0.0621\n",
            "Epoch 4/100\n",
            "384/384 [==============================] - 1s 3ms/step - loss: 0.0628 - mae: 0.0628 - val_loss: 0.0626 - val_mae: 0.0626\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Evaluate the model"
      ],
      "metadata": {
        "id": "yVFGcuYGS02F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.evaluate(test_windows, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYgI89InTq2k",
        "outputId": "a7db32ee-7e2d-491e-eed4-a727f2443b6a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0639 - mae: 0.0639\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.06387052685022354, 0.06387052685022354]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Making forecasts with a model (on the test dataset)"
      ],
      "metadata": {
        "id": "AJX8DMdaT-fD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we're going to be running more modelling experiments, let's write a function which:\n",
        "\n",
        "1. Takes in a trained model (just like model_1)\n",
        "\n",
        "2. Takes in some input data (just like the data the model was trained on)\n",
        "\n",
        "3. Passes the input data to the model's predict() method\n",
        "\n",
        "4. Returns the predictions"
      ],
      "metadata": {
        "id": "wyrQYcDGUEMf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_preds(model, input_data):\n",
        "  \"\"\"\n",
        "  Uses model to make predictions on input_data.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  model: trained model \n",
        "  input_data: windowed input data (same kind of data model was trained on)\n",
        "\n",
        "  Returns model predictions on input_data.\n",
        "  \"\"\"\n",
        "  forecast = model.predict(input_data)\n",
        "  return tf.squeeze(forecast) # return 1D array of predictions"
      ],
      "metadata": {
        "id": "zgturGU6UU4E"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's use our make_preds() and see how it goes."
      ],
      "metadata": {
        "id": "gG7LNbmZUYE-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_preds = make_preds(model_0, test_windows)\n",
        "model_0_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9C6fnQnFUs6B",
        "outputId": "f7b9e979-4832-462c-9e87-b7db99aa8d7c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([-0.7687543 , -0.84852   , -0.8310496 , -0.80807984, -0.8218377 ,\n",
              "       -0.84454507, -0.85833555, -0.8470921 , -0.8527563 , -0.8637108 ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results = evaluate_preds(tf.squeeze(test_labels), model_0_preds)\n",
        "model_0_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVNR6hK3U6u8",
        "outputId": "95ae6c57-07e0-4e2e-a135-a558cfa94578"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 0.06387054,\n",
              " 'mape': 46.699543,\n",
              " 'mase': 0.74981046,\n",
              " 'mse': 0.008488747,\n",
              " 'rmse': 0.0921344}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We beat the baseline!"
      ],
      "metadata": {
        "id": "-dV9qYp3VPe2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Lets plot model_0"
      ],
      "metadata": {
        "id": "-hAGiEDhVe2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "# Account for the test_window offset and index into test_labels to ensure correct plotting\n",
        "plot_time_series(timesteps=date_time[-len(test_windows):], \n",
        "                 values=test_labels[:, 0], \n",
        "                 start=-100, \n",
        "                 label=\"Test Data\")\n",
        "\n",
        "plot_time_series(timesteps=date_time[-len(test_windows):],\n",
        "                 values=model_0_preds,\n",
        "                 start=-100, \n",
        "                 format=\"-\",\n",
        "                 label=\"model_0_preds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QnpM-IJHVw6G",
        "outputId": "dedca84c-bad6-478a-8a51-1d3623bd0156"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Plot model loss curves"
      ],
      "metadata": {
        "id": "jOLDscEeWI6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the validation and training data separately\n",
        "def plot_loss_curves(history):\n",
        "  \"\"\"\n",
        "  Returns separate loss curves for training and validation metrics.\n",
        "  \"\"\" \n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "\n",
        "  # accuracy = history.history['accuracy']\n",
        "  # val_accuracy = history.history['val_accuracy']\n",
        "\n",
        "  epochs = range(len(history.history['loss']))\n",
        "\n",
        "  # Plot loss\n",
        "  plt.plot(epochs, loss, label='training_loss')\n",
        "  plt.plot(epochs, val_loss, label='val_loss')\n",
        "  plt.title('Loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.legend()\n"
      ],
      "metadata": {
        "id": "T2R78jPXYGR5"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss_curves(history_0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UKysYb3UYPyg",
        "outputId": "761ddbdc-fd23-44ea-f246-546bf523a534"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##PART II"
      ],
      "metadata": {
        "id": "kPXM4VX3YT3Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets redo the above but with tensorflows `tf.keras.utils.timeseries_dataset_from_array`"
      ],
      "metadata": {
        "id": "XnD0wYhwYxeS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Create Train, Val & Test tf.data.Datasets"
      ],
      "metadata": {
        "id": "eJdziRbQZJjo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Train"
      ],
      "metadata": {
        "id": "2qcLlfmCaZ1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_target_slice = train_temp_arr.size-WINDOW_SIZE\n",
        "train_tfds = tf.keras.utils.timeseries_dataset_from_array(data=train_temp_arr,\n",
        "                                                  targets=train_temp_arr[WINDOW_SIZE:],\n",
        "                                                  sequence_stride=1,\n",
        "                                                  shuffle=False,\n",
        "                                                  sequence_length=WINDOW_SIZE,\n",
        "                                                  batch_size=32)\n",
        "\n",
        "sub_train_tfds = train_tfds.take(2)\n",
        "\n",
        "for window in sub_train_tfds:\n",
        "  print(window)"
      ],
      "metadata": {
        "id": "JsuzsLbkagfw",
        "outputId": "a160eaed-28ba-477e-e6a3-98daf44d9bfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(<tf.Tensor: shape=(32, 3), dtype=float64, numpy=\n",
            "array([[-1.98247323, -2.07837211, -2.07028426],\n",
            "       [-2.07837211, -2.07028426, -2.09801405],\n",
            "       [-2.07028426, -2.09801405, -2.16502773],\n",
            "       [-2.09801405, -2.16502773, -2.16964936],\n",
            "       [-2.16502773, -2.16964936, -2.11187895],\n",
            "       [-2.16964936, -2.11187895, -1.98825027],\n",
            "       [-2.11187895, -1.98825027, -1.93741231],\n",
            "       [-1.98825027, -1.93741231, -1.86577701],\n",
            "       [-1.93741231, -1.86577701, -1.90852711],\n",
            "       [-1.86577701, -1.90852711, -1.84613507],\n",
            "       [-1.90852711, -1.84613507, -1.73290507],\n",
            "       [-1.84613507, -1.73290507, -1.73868211],\n",
            "       [-1.73290507, -1.73868211, -1.7097969 ],\n",
            "       [-1.73868211, -1.7097969 , -1.67629006],\n",
            "       [-1.7097969 , -1.67629006, -1.67282384],\n",
            "       [-1.67629006, -1.67282384, -1.65895894],\n",
            "       [-1.67282384, -1.65895894, -1.64278323],\n",
            "       [-1.65895894, -1.64278323, -1.61851966],\n",
            "       [-1.64278323, -1.61851966, -1.60696557],\n",
            "       [-1.61851966, -1.60696557, -1.57230333],\n",
            "       [-1.60696557, -1.57230333, -1.5688371 ],\n",
            "       [-1.57230333, -1.5688371 , -1.57692496],\n",
            "       [-1.5688371 , -1.57692496, -1.56537088],\n",
            "       [-1.57692496, -1.56537088, -1.54803976],\n",
            "       [-1.56537088, -1.54803976, -1.56652629],\n",
            "       [-1.54803976, -1.56652629, -1.58154659],\n",
            "       [-1.56652629, -1.58154659, -1.6254521 ],\n",
            "       [-1.58154659, -1.6254521 , -1.56421547],\n",
            "       [-1.6254521 , -1.56421547, -1.54688435],\n",
            "       [-1.56421547, -1.54688435, -1.55266139],\n",
            "       [-1.54688435, -1.55266139, -1.52955323],\n",
            "       [-1.55266139, -1.52955323, -1.50644506]])>, <tf.Tensor: shape=(32,), dtype=float64, numpy=\n",
            "array([-2.09801405, -2.16502773, -2.16964936, -2.11187895, -1.98825027,\n",
            "       -1.93741231, -1.86577701, -1.90852711, -1.84613507, -1.73290507,\n",
            "       -1.73868211, -1.7097969 , -1.67629006, -1.67282384, -1.65895894,\n",
            "       -1.64278323, -1.61851966, -1.60696557, -1.57230333, -1.5688371 ,\n",
            "       -1.57692496, -1.56537088, -1.54803976, -1.56652629, -1.58154659,\n",
            "       -1.6254521 , -1.56421547, -1.54688435, -1.55266139, -1.52955323,\n",
            "       -1.50644506, -1.47062741])>)\n",
            "(<tf.Tensor: shape=(32, 3), dtype=float64, numpy=\n",
            "array([[-1.52955323, -1.50644506, -1.47062741],\n",
            "       [-1.50644506, -1.47062741, -1.412857  ],\n",
            "       [-1.47062741, -1.412857  , -1.34468792],\n",
            "       [-1.412857  , -1.34468792, -1.34815414],\n",
            "       [-1.34468792, -1.34815414, -1.29731618],\n",
            "       [-1.34815414, -1.29731618, -1.3712623 ],\n",
            "       [-1.29731618, -1.3712623 , -1.3851272 ],\n",
            "       [-1.3712623 , -1.3851272 , -1.40707996],\n",
            "       [-1.3851272 , -1.40707996, -1.43827598],\n",
            "       [-1.40707996, -1.43827598, -1.43365435],\n",
            "       [-1.43827598, -1.43365435, -1.4556071 ],\n",
            "       [-1.43365435, -1.4556071 , -1.51684374],\n",
            "       [-1.4556071 , -1.51684374, -1.55843843],\n",
            "       [-1.51684374, -1.55843843, -1.5965669 ],\n",
            "       [-1.55843843, -1.5965669 , -1.66242517],\n",
            "       [-1.5965669 , -1.66242517, -1.77218894],\n",
            "       [-1.66242517, -1.77218894, -1.76063486],\n",
            "       [-1.77218894, -1.76063486, -1.76987813],\n",
            "       [-1.76063486, -1.76987813, -1.86346619],\n",
            "       [-1.76987813, -1.86346619, -1.99980436],\n",
            "       [-1.86346619, -1.99980436, -2.03215579],\n",
            "       [-1.99980436, -2.03215579, -2.12458844],\n",
            "       [-2.03215579, -2.12458844, -2.14538579],\n",
            "       [-2.12458844, -2.14538579, -2.03793283],\n",
            "       [-2.14538579, -2.03793283, -1.96167589],\n",
            "       [-2.03793283, -1.96167589, -1.74099292],\n",
            "       [-1.96167589, -1.74099292, -1.37819475],\n",
            "       [-1.74099292, -1.37819475, -1.3007824 ],\n",
            "       [-1.37819475, -1.3007824 , -1.14711312],\n",
            "       [-1.3007824 , -1.14711312, -1.37588394],\n",
            "       [-1.14711312, -1.37588394, -1.59425608],\n",
            "       [-1.37588394, -1.59425608, -1.52377618]])>, <tf.Tensor: shape=(32,), dtype=float64, numpy=\n",
            "array([-1.412857  , -1.34468792, -1.34815414, -1.29731618, -1.3712623 ,\n",
            "       -1.3851272 , -1.40707996, -1.43827598, -1.43365435, -1.4556071 ,\n",
            "       -1.51684374, -1.55843843, -1.5965669 , -1.66242517, -1.77218894,\n",
            "       -1.76063486, -1.76987813, -1.86346619, -1.99980436, -2.03215579,\n",
            "       -2.12458844, -2.14538579, -2.03793283, -1.96167589, -1.74099292,\n",
            "       -1.37819475, -1.3007824 , -1.14711312, -1.37588394, -1.59425608,\n",
            "       -1.52377618, -1.48911394])>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Val"
      ],
      "metadata": {
        "id": "gAe8izAoa9u3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "val_tfds = tf.keras.utils.timeseries_dataset_from_array(data=val_temp_arr,\n",
        "                                                  targets=val_temp_arr[WINDOW_SIZE:],\n",
        "                                                  sequence_stride=1,\n",
        "                                                  shuffle=False,\n",
        "                                                  sequence_length=WINDOW_SIZE,\n",
        "                                                  batch_size=32)"
      ],
      "metadata": {
        "id": "_K1MoBdKbO3g"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Test"
      ],
      "metadata": {
        "id": "Qf3kgazucDvu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_tfds = tf.keras.utils.timeseries_dataset_from_array(data=test_temp_arr,\n",
        "                                                  targets=test_temp_arr[WINDOW_SIZE:],\n",
        "                                                  sequence_stride=1,\n",
        "                                                  shuffle=False,\n",
        "                                                  sequence_length=WINDOW_SIZE,\n",
        "                                                  batch_size=32)"
      ],
      "metadata": {
        "id": "lRNfJhG0cH5I"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Performance"
      ],
      "metadata": {
        "id": "n3f3Zv_mcRJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_tfds = train_tfds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "val_tfds = val_tfds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "test_tfds = test_tfds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "metadata": {
        "id": "97e7siwBcY2B"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Create new model"
      ],
      "metadata": {
        "id": "WnxxglgjcmHj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = tf.keras.Sequential([\n",
        "  layers.Input(shape=(WINDOW_SIZE)),                             \n",
        "  layers.Dense(32, activation='relu'),\n",
        "  layers.Dense(32, activation='relu'),\n",
        "  layers.Dense(HORIZON)\n",
        "])\n",
        "\n",
        "model_1.compile(loss='mae',\n",
        "                optimizer=tf.optimizers.Adam(),\n",
        "                metrics=['mae'])"
      ],
      "metadata": {
        "id": "Y9iLpGH5cp8g"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_1 = model_1.fit(train_tfds,\n",
        "                      epochs=100,\n",
        "                      verbose=1,\n",
        "                      batch_size=128,\n",
        "                      validation_data=val_tfds,\n",
        "                      callbacks=[early_stopping])"
      ],
      "metadata": {
        "id": "D9_ySGSDcsqB",
        "outputId": "0817e317-1b55-420b-ca16-8cf92c0b1bf6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1534/1534 [==============================] - 11s 7ms/step - loss: 0.0847 - mae: 0.0847 - val_loss: 0.0711 - val_mae: 0.0711\n",
            "Epoch 2/100\n",
            "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0667 - mae: 0.0667 - val_loss: 0.0746 - val_mae: 0.0746\n",
            "Epoch 3/100\n",
            "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0670 - mae: 0.0670 - val_loss: 0.0715 - val_mae: 0.0715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluate Model"
      ],
      "metadata": {
        "id": "MwqXW7aMejNw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.evaluate(test_tfds)"
      ],
      "metadata": {
        "id": "rVVIiO3pc1Ma",
        "outputId": "f8dcd9b7-914e-470d-d93d-6a6b33881e66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "219/219 [==============================] - 1s 5ms/step - loss: 0.0693 - mae: 0.0693\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.06933049857616425, 0.06933049857616425]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_preds = model_1.predict(test_tfds)"
      ],
      "metadata": {
        "id": "b1iqQfmmegUV"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Plot predictions"
      ],
      "metadata": {
        "id": "mZj_o-yLevKJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "# Account for the test_window offset and index into test_labels to ensure correct plotting\n",
        "plot_time_series(timesteps=date_time[-len(model_1_preds):], \n",
        "                 values=test_labels[:, 0], \n",
        "                 start=-100, \n",
        "                 label=\"Test Data\")\n",
        "\n",
        "plot_time_series(timesteps=date_time[-len(model_1_preds):],\n",
        "                 values=model_1_preds,\n",
        "                 start=-100, \n",
        "                 format=\"-\",\n",
        "                 label=\"Model 1 predictions\")"
      ],
      "metadata": {
        "id": "yuVIW-Nee0Vb",
        "outputId": "89b74a2e-0ec1-47c7-9671-b1f0886741f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results = evaluate_preds(y_true=test_temp_arr[WINDOW_SIZE:],\n",
        "                               y_pred=tf.squeeze(model_1_preds))\n",
        "model_1_results, model_0_results"
      ],
      "metadata": {
        "id": "JR7vAZPkfMv4",
        "outputId": "810dd06f-c766-41b1-d8fc-5d7ca9b452a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'mae': 0.0693305,\n",
              "  'mape': 47.674255,\n",
              "  'mase': 0.8139078,\n",
              "  'mse': 0.009396407,\n",
              "  'rmse': 0.09693506},\n",
              " {'mae': 0.06387054,\n",
              "  'mape': 46.699543,\n",
              "  'mase': 0.74981046,\n",
              "  'mse': 0.008488747,\n",
              "  'rmse': 0.0921344})"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sP59yBmr_2ZZ"
      },
      "execution_count": 46,
      "outputs": []
    }
  ],
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      "language": "python",
      "name": "python3"
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