midterm.ipynb

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      "source": [
        "1. Work Process\n",
        "\n",
        "1) Data Collection\n",
        "- Total nine variables: Macro (ip, US and EU money),index (DJIA), currency (Norway Krona (inverse included), Euro), Coal price, US interest rate (10Yr)\n",
        "\n",
        "2) Preprocessing\n",
        "- ARIMA: Stationarity, Residual, p.d.q\n",
        "- LSTM: Training-Test Split, validation\n",
        "\n",
        "3) Model Selection\n",
        "- ARIMA, ML. DL\n",
        "\n",
        "4) Evaluation\n",
        "a. ARIMA:  \n",
        " - MSE: 1907.437516093254\n",
        " - MAE: 1400.4750000000004\n",
        " - MAPE: 0.04530564449162539\n",
        "\n",
        "b. LSTM\n",
        " - MSE: 2203.1874311174174\n",
        " - MAE: 1569.380260416665\n",
        " - MAPE: 0.05147524072407283\n",
        "\n",
        "5) Result\n",
        " To compare the performance of ARIMA and LSTM shows that of ARIMA is slightly better than LSTM with eight valriables with indicators like MSE, MAE, and MAPE. \n",
        "ARIMA has accuracy of 95.5% and LSTM 94.9% with 1907 and 2203 of MSE each. \n",
        "\n",
        "2. Blog address\n",
        " https://github.com/pangsoo/midterm.git"
      ],
      "metadata": {
        "id": "7m_tapp-2Ml7"
      }
    },
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      },
      "outputs": [
        {
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.graphics.tsaplots import plot_predict\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from google.colab import drive\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "import math\n",
        "import time\n",
        "import datetime\n",
        "\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://raw.githubusercontent.com/pangsoo/midterm/main/middata.csv\"\n",
        "df = pd.read_csv(url)\n",
        "df"
      ],
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              "        date        dji     eur  10yr_R  eu_money  us_money        ip    coal  \\\n",
              "0    2010-01  10,067.33  1.3863   3.588      1155    1995.0   89.1936   95.30   \n",
              "1    2010-02  10,325.26  1.3626   3.619      1163    2115.2   89.5089   93.85   \n",
              "2    2010-03  10,856.63  1.3512   3.833      1183    2079.6   90.1449   94.95   \n",
              "3    2010-04  11,008.61  1.3298   3.659      1206    2014.4   90.4740  103.85   \n",
              "4    2010-05  10,136.63  1.2306   3.303      1227    2012.3   91.7131  100.20   \n",
              "..       ...        ...     ...     ...       ...       ...       ...     ...   \n",
              "152  2022-09  28,730.12  0.9799   3.829      6228    5410.9  104.5740  433.70   \n",
              "153  2022-10  32,734.40  0.9883   4.050      6307    5339.6  104.5927  356.30   \n",
              "154  2022-11  34,587.46  1.0405   3.611      6386    5418.7  103.9846  397.00   \n",
              "155  2022-12  33,147.28  1.0702   3.879      6296    5405.4  102.9256  404.15   \n",
              "156  2023-01  34,086.89  1.0861   3.510      6054    5328.4  102.9525  251.75   \n",
              "\n",
              "         nok   usd-nok  \n",
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              "..       ...       ...  \n",
              "152  10.8821  0.091894  \n",
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              "154   9.8382  0.101645  \n",
              "155   9.7965  0.102077  \n",
              "156   9.9846  0.100154  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 157 entries, 0 to 156\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   date      157 non-null    object \n",
            " 1   dji       157 non-null    object \n",
            " 2   eur       157 non-null    float64\n",
            " 3   10yr_R    157 non-null    float64\n",
            " 4   eu_money  157 non-null    int64  \n",
            " 5   us_money  157 non-null    float64\n",
            " 6   ip        157 non-null    float64\n",
            " 7   coal      157 non-null    float64\n",
            " 8   nok       157 non-null    float64\n",
            " 9   usd-nok   157 non-null    float64\n",
            "dtypes: float64(7), int64(1), object(2)\n",
            "memory usage: 12.4+ KB\n"
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        "# Conver to the time series\n",
        "# Set index as time\n",
        "\n",
        "df['date'] = pd.to_datetime(df['date'], format = '%Y-%m')\n",
        "df.index = df['date']\n",
        "df.set_index('date', inplace = True)\n"
      ],
      "metadata": {
        "id": "HaPzqK4LQb3-"
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "df['dji'] = df['dji'].str.replace(',', '').astype(float)\n",
        "df.head()"
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              "                 dji     eur  10yr_R  eu_money  us_money       ip    coal  \\\n",
              "date                                                                        \n",
              "2010-01-01  10067.33  1.3863   3.588      1155    1995.0  89.1936   95.30   \n",
              "2010-02-01  10325.26  1.3626   3.619      1163    2115.2  89.5089   93.85   \n",
              "2010-03-01  10856.63  1.3512   3.833      1183    2079.6  90.1449   94.95   \n",
              "2010-04-01  11008.61  1.3298   3.659      1206    2014.4  90.4740  103.85   \n",
              "2010-05-01  10136.63  1.2306   3.303      1227    2012.3  91.7131  100.20   \n",
              "\n",
              "               nok   usd-nok  \n",
              "date                          \n",
              "2010-01-01  5.9270  0.168719  \n",
              "2010-02-01  5.9058  0.169325  \n",
              "2010-03-01  5.9395  0.168364  \n",
              "2010-04-01  5.9060  0.169319  \n",
              "2010-05-01  6.4550  0.154919  "
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              "      <th>date</th>\n",
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              "      <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>2010-01-01</th>\n",
              "      <td>10067.33</td>\n",
              "      <td>1.3863</td>\n",
              "      <td>3.588</td>\n",
              "      <td>1155</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>89.1936</td>\n",
              "      <td>95.30</td>\n",
              "      <td>5.9270</td>\n",
              "      <td>0.168719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-01</th>\n",
              "      <td>10325.26</td>\n",
              "      <td>1.3626</td>\n",
              "      <td>3.619</td>\n",
              "      <td>1163</td>\n",
              "      <td>2115.2</td>\n",
              "      <td>89.5089</td>\n",
              "      <td>93.85</td>\n",
              "      <td>5.9058</td>\n",
              "      <td>0.169325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-03-01</th>\n",
              "      <td>10856.63</td>\n",
              "      <td>1.3512</td>\n",
              "      <td>3.833</td>\n",
              "      <td>1183</td>\n",
              "      <td>2079.6</td>\n",
              "      <td>90.1449</td>\n",
              "      <td>94.95</td>\n",
              "      <td>5.9395</td>\n",
              "      <td>0.168364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-04-01</th>\n",
              "      <td>11008.61</td>\n",
              "      <td>1.3298</td>\n",
              "      <td>3.659</td>\n",
              "      <td>1206</td>\n",
              "      <td>2014.4</td>\n",
              "      <td>90.4740</td>\n",
              "      <td>103.85</td>\n",
              "      <td>5.9060</td>\n",
              "      <td>0.169319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-05-01</th>\n",
              "      <td>10136.63</td>\n",
              "      <td>1.2306</td>\n",
              "      <td>3.303</td>\n",
              "      <td>1227</td>\n",
              "      <td>2012.3</td>\n",
              "      <td>91.7131</td>\n",
              "      <td>100.20</td>\n",
              "      <td>6.4550</td>\n",
              "      <td>0.154919</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-003482dc-c8a0-4514-ad23-c1a2807c88fb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-003482dc-c8a0-4514-ad23-c1a2807c88fb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-003482dc-c8a0-4514-ad23-c1a2807c88fb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssitKOWNeM4c",
        "outputId": "796cb183-e157-48ee-c775-99f869bbdd0b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 157 entries, 2010-01-01 to 2023-01-01\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   dji       157 non-null    float64\n",
            " 1   eur       157 non-null    float64\n",
            " 2   10yr_R    157 non-null    float64\n",
            " 3   eu_money  157 non-null    int64  \n",
            " 4   us_money  157 non-null    float64\n",
            " 5   ip        157 non-null    float64\n",
            " 6   coal      157 non-null    float64\n",
            " 7   nok       157 non-null    float64\n",
            " 8   usd-nok   157 non-null    float64\n",
            "dtypes: float64(8), int64(1)\n",
            "memory usage: 12.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "7C01xf67zIkF",
        "outputId": "c4ea5715-f232-4b22-d900-5ae736e8834e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock = df.iloc[:, [0]]\n",
        "stock.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "C0n_fZbESx6R",
        "outputId": "b72e37ef-a120-408b-9080-b54c57c12a28"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 dji\n",
              "date                \n",
              "2022-09-01  28730.12\n",
              "2022-10-01  32734.40\n",
              "2022-11-01  34587.46\n",
              "2022-12-01  33147.28\n",
              "2023-01-01  34086.89"
            ],
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              "\n",
              "  <div id=\"df-c37a7117-a107-4d34-96b0-0008006219f7\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>dji</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-09-01</th>\n",
              "      <td>28730.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-01</th>\n",
              "      <td>32734.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-11-01</th>\n",
              "      <td>34587.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-01</th>\n",
              "      <td>33147.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-01</th>\n",
              "      <td>34086.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c37a7117-a107-4d34-96b0-0008006219f7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c37a7117-a107-4d34-96b0-0008006219f7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERNHd6b9cMqf",
        "outputId": "5a8b4fa5-541d-47ff-b2dc-c06905360f0f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 157 entries, 2010-01-01 to 2023-01-01\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   dji     157 non-null    float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 2.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ADF check\n",
        "\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "stock_diff1 = stock.diff().dropna()['dji']\n",
        "result = adfuller(stock)\n",
        "\n",
        "print(f'Orginal ADF Stat: {result[0]: .3f}')\n",
        "print(f'Orginal p-value: {result[1]: .3f}')\n",
        "\n",
        "result = adfuller(stock_diff1)\n",
        "print(f'1st Diff ADF Stat: {result[0]: .3f}')\n",
        "print(f'1st Diff p-value: {result[1]: .3f}') # P-value indicating 1st diff is stationary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JihdGvLXrPf",
        "outputId": "733d93b8-c318-418f-82dd-1b83f9ea1652"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Orginal ADF Stat: -0.332\n",
            "Orginal p-value:  0.921\n",
            "1st Diff ADF Stat: -4.294\n",
            "1st Diff p-value:  0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pmdarima\n",
        "from pmdarima.arima import ndiffs\n",
        "import pmdarima as pm\n",
        "\n",
        "stock_train = stock[:int(0.95*len(stock))]\n",
        "stock_test = stock[int(0.95*len(stock)):]\n",
        "stock_train.plot()\n",
        "stock_test.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "vWagUC4fap4i",
        "outputId": "b30a944d-cfdb-4bfb-fde4-3453adc73217"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.9/dist-packages (2.0.2)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.2.1)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.22.4)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (57.4.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.10.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.26.14)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (0.29.33)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.2.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (1.3.5)\n",
            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.9/dist-packages (from pmdarima) (0.13.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.19->pmdarima) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.22->pmdarima) (3.1.0)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.9/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.9/dist-packages (from statsmodels>=0.13.2->pmdarima) (23.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from patsy>=0.5.2->statsmodels>=0.13.2->pmdarima) (1.15.0)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='date'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_train.shape)\n",
        "stock_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "uCby_Yj_3MLg",
        "outputId": "bde4ecfb-8cd6-4aed-a0b8-57a691a73f32"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(149, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 dji\n",
              "date                \n",
              "2022-06-01  30779.71\n",
              "2022-07-01  32846.45\n",
              "2022-08-01  31511.09\n",
              "2022-09-01  28730.12\n",
              "2022-10-01  32734.40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-577c3044-1dd0-4729-9a59-37a477a363e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>dji</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-06-01</th>\n",
              "      <td>30779.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-01</th>\n",
              "      <td>32846.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-01</th>\n",
              "      <td>31511.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-09-01</th>\n",
              "      <td>28730.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-01</th>\n",
              "      <td>32734.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-577c3044-1dd0-4729-9a59-37a477a363e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-577c3044-1dd0-4729-9a59-37a477a363e3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = pm.auto_arima(y = stock_train,\n",
        "                      d=1,\n",
        "                      start_p = 0,\n",
        "                      max_p = 3,\n",
        "                      start_q = 0,\n",
        "                      max_q = 3,\n",
        "                      m =1,\n",
        "                      seasonal=False,\n",
        "                      stepwise = True,\n",
        "                      trace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMTUJnX3j-ZG",
        "outputId": "3d6018d0-f3b5-4d84-d596-0c47038684bd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2432.255, Time=0.14 sec\n",
            " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2432.727, Time=0.10 sec\n",
            " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2432.545, Time=0.15 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0]             : AIC=2434.733, Time=0.07 sec\n",
            " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=1.11 sec\n",
            "\n",
            "Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n",
            "Total fit time: 1.580 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = pm.auto_arima (stock_train, d = 1, seasonal = False, trace = True)\n",
        "model.fit(stock_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "DJBFkDg2vb7W",
        "outputId": "8c46cf40-48e6-40d4-d0fa-e61e7aa9de38"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=2.52 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2432.255, Time=0.14 sec\n",
            " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2432.727, Time=0.10 sec\n",
            " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2432.545, Time=0.30 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0]             : AIC=2434.733, Time=0.07 sec\n",
            " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=inf, Time=1.17 sec\n",
            "\n",
            "Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n",
            "Total fit time: 4.328 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(order=(0, 1, 0), scoring_args={}, suppress_warnings=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre> ARIMA(0,1,0)(0,0,0)[0] intercept</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ARIMA</label><div class=\"sk-toggleable__content\"><pre> ARIMA(0,1,0)(0,0,0)[0] intercept</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = ARIMA(stock_train, order=(0,1,0))\n",
        "model_fit = model.fit()\n",
        "print(model_fit.summary())\n",
        "\n",
        "# Prob(Q) = 0.16 > 0.05 then we can decide the residual is white noise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRqYXHsVv1PK",
        "outputId": "8721638d-b915-469b-8993-93d379b8a53b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                    dji   No. Observations:                  149\n",
            "Model:                 ARIMA(0, 1, 0)   Log Likelihood               -1216.366\n",
            "Date:                Sun, 12 Mar 2023   AIC                           2434.733\n",
            "Time:                        15:01:07   BIC                           2437.730\n",
            "Sample:                    01-01-2010   HQIC                          2435.950\n",
            "                         - 05-01-2022                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "sigma2      8.003e+05   6.23e+04     12.854      0.000    6.78e+05    9.22e+05\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   1.94   Jarque-Bera (JB):                58.70\n",
            "Prob(Q):                              0.16   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               8.34   Skew:                            -0.51\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.91\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.9/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.9/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.plot_diagnostics(figsize = (16,8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "d3L0D0SLxHM5",
        "outputId": "9a223dcf-08ce-4814-d784-f343751d7ce5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict = model_fit.predict(n_periods = len(stock_test))\n",
        "predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERhI0q1DzuhM",
        "outputId": "acb01673-2ab1-4919-b17a-4f8af8d4b400"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2010-01-01        0.00\n",
              "2010-02-01    10067.33\n",
              "2010-03-01    10325.26\n",
              "2010-04-01    10856.63\n",
              "2010-05-01    11008.61\n",
              "                ...   \n",
              "2022-01-01    36338.30\n",
              "2022-02-01    35131.86\n",
              "2022-03-01    33892.60\n",
              "2022-04-01    34678.35\n",
              "2022-05-01    32977.21\n",
              "Freq: MS, Name: predicted_mean, Length: 149, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPLDAtnG2eA8",
        "outputId": "ca8d6fb9-4bfb-4fcd-e187-b8ee684f4843"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 dji\n",
            "date                \n",
            "2010-01-01  10067.33\n",
            "2010-02-01  10325.26\n",
            "2010-03-01  10856.63\n",
            "2010-04-01  11008.61\n",
            "2010-05-01  10136.63\n",
            "...              ...\n",
            "2022-01-01  35131.86\n",
            "2022-02-01  33892.60\n",
            "2022-03-01  34678.35\n",
            "2022-04-01  32977.21\n",
            "2022-05-01  32991.97\n",
            "\n",
            "[149 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predict(model_fit, dynamic=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "x_GruzZK3mzX",
        "outputId": "af73048c-24c3-48ca-bdf8-740f890c786c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model_fit.forecast(steps = len(stock_test))\n",
        "forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSgt6tqE6ULp",
        "outputId": "fdd459c9-4eeb-45df-9eab-be8e03badd13"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2022-06-01    32991.97\n",
              "2022-07-01    32991.97\n",
              "2022-08-01    32991.97\n",
              "2022-09-01    32991.97\n",
              "2022-10-01    32991.97\n",
              "2022-11-01    32991.97\n",
              "2022-12-01    32991.97\n",
              "2023-01-01    32991.97\n",
              "Freq: MS, Name: predicted_mean, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_forecast = forecast.to_frame()\n",
        "df_forecast.columns = ['dji_forecast']\n",
        "df_forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oV2RffaaSBYb",
        "outputId": "5e055a40-8190-49f9-9dc8-9b58e75976be"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            dji_forecast\n",
              "2022-06-01      32991.97\n",
              "2022-07-01      32991.97\n",
              "2022-08-01      32991.97\n",
              "2022-09-01      32991.97\n",
              "2022-10-01      32991.97\n",
              "2022-11-01      32991.97\n",
              "2022-12-01      32991.97\n",
              "2023-01-01      32991.97"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>2022-06-01</th>\n",
              "      <td>32991.97</td>\n",
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              "      <td>32991.97</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-01</th>\n",
              "      <td>32991.97</td>\n",
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              "      <th>2023-01-01</th>\n",
              "      <td>32991.97</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8d76f00-11f9-49bc-8803-5d77c8258dc5')\"\n",
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      "source": [
        "compare = pd.merge(stock_test, df_forecast, how='inner', left_index = True, right_index=True)\n",
        "compare.head() # The forecast is explaied flat because the trend of the recent months remain silent"
      ],
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      "execution_count": 29,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 dji  dji_forecast\n",
              "date                              \n",
              "2022-06-01  30779.71      32991.97\n",
              "2022-07-01  32846.45      32991.97\n",
              "2022-08-01  31511.09      32991.97\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance\n",
        "mse_arima = mean_squared_error(stock_test['dji'].values, forecast.values, squared=False)\n",
        "print('MSE: '+str(mse_arima))\n",
        "mae_arima = mean_absolute_error(stock_test['dji'].values, forecast.values)\n",
        "print('MAE: '+str(mae_arima))\n",
        "mape_arima = np.mean(np.abs(compare['dji_forecast'] - compare['dji'])/np.abs(compare['dji']))\n",
        "print('MAPE: '+str(mape_arima)) # 4.5% of absolute gap\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tno72cv57Y83",
        "outputId": "dc3bdf65-716c-45a6-cab8-66c60c81cbb9"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 1907.437516093254\n",
            "MAE: 1400.4750000000004\n",
            "MAPE: 0.04530564449162539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xo8J1XzPwkp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Stock Forecast with LSTM**"
      ],
      "metadata": {
        "id": "anfHmBll7KFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn\n",
        "!pip install tensor\n",
        "import tensorflow as tf\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearm.metrics import mean_squared_error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7x0JbofmXJe3",
        "outputId": "6b1fc8a3-3714-4e11-e223-64433a95e643"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Created wheel for tensor: filename=tensor-0.3.6-py3-none-any.whl size=71569 sha256=24fcf35b1b8af4e00ce6777157e4b6fc3b24d39234160b63e42426d0901d4570\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/86/de/a812020c1b6bac75652227a1c315400422857adba7252ff69f\n",
            "  Building wheel for construct (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for construct: filename=construct-2.10.68-py3-none-any.whl size=59226 sha256=51ffdbb1bfe9d410a4fada6c6736f50793636f2c6dc74ed1d1d55ce721d25e9a\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/1c/64/ddaac89a721654e15e30ae193023c26da4c8677547cb38268b\n",
            "Successfully built tensor construct\n",
            "Installing collected packages: ply, incremental, constantly, zope.interface, pysmi, pycryptodomex, hyperlink, construct, Automat, Twisted, pysnmp, tensor\n",
            "Successfully installed Automat-22.10.0 Twisted-22.10.0 constantly-15.1.0 construct-2.10.68 hyperlink-21.0.0 incremental-22.10.0 ply-3.11 pycryptodomex-3.17 pysmi-0.3.4 pysnmp-4.4.12 tensor-0.3.6 zope.interface-5.5.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://raw.githubusercontent.com/pangsoo/midterm/main/middata.csv\"\n",
        "df = pd.read_csv(url)\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5IXpRiuuasg",
        "outputId": "3b55905e-b964-4e4a-e949-580f3be3650d"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      date        dji     eur  10yr_R  eu_money  us_money       ip    coal  \\\n",
            "0  2010-01  10,067.33  1.3863   3.588      1155    1995.0  89.1936   95.30   \n",
            "1  2010-02  10,325.26  1.3626   3.619      1163    2115.2  89.5089   93.85   \n",
            "2  2010-03  10,856.63  1.3512   3.833      1183    2079.6  90.1449   94.95   \n",
            "3  2010-04  11,008.61  1.3298   3.659      1206    2014.4  90.4740  103.85   \n",
            "4  2010-05  10,136.63  1.2306   3.303      1227    2012.3  91.7131  100.20   \n",
            "\n",
            "      nok   usd-nok  \n",
            "0  5.9270  0.168719  \n",
            "1  5.9058  0.169325  \n",
            "2  5.9395  0.168364  \n",
            "3  5.9060  0.169319  \n",
            "4  6.4550  0.154919  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns = ['date'])\n",
        "df['dji'] = df['dji'].str.replace(',', '').astype(float)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "pNB-lLQjutdA",
        "outputId": "aebd30e3-4dfc-46d6-cca6-dada3c2c5650"
      },
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        dji     eur  10yr_R  eu_money  us_money       ip    coal     nok  \\\n",
              "0  10067.33  1.3863   3.588      1155    1995.0  89.1936   95.30  5.9270   \n",
              "1  10325.26  1.3626   3.619      1163    2115.2  89.5089   93.85  5.9058   \n",
              "2  10856.63  1.3512   3.833      1183    2079.6  90.1449   94.95  5.9395   \n",
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              "4  10136.63  1.2306   3.303      1227    2012.3  91.7131  100.20  6.4550   \n",
              "\n",
              "    usd-nok  \n",
              "0  0.168719  \n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-6bc89340-f6cf-4adf-a90d-1ee3500677fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6bc89340-f6cf-4adf-a90d-1ee3500677fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setting Target \n",
        "\n",
        "target_list = ['dji', 'eur']\n",
        "target = target_list[0]\n",
        "\n",
        "lag = 6\n",
        "WINDOW_SIZE= 6  # reading 6mo in order to forecast 6mo \n",
        "\n",
        "def make_dataset(data, label, window_size = WINDOW_SIZE):\n",
        "  feature_list = []\n",
        "  label_list = []\n",
        "  for i in range(len(data) - window_size):\n",
        "    feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
        "    label_list.append(np.array(label.iloc[i+window_size]))\n",
        "  return np.array(feature_list), np.array(label_list)"
      ],
      "metadata": {
        "id": "4WmG3IPtRtXG"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "mini = min(df[target])\n",
        "maxi = max(df[target])\n",
        "scale = maxi - mini\n",
        "\n",
        "scaled = scaler.fit_transform(df)\n",
        "columns = df.columns\n",
        "df = pd.DataFrame(scaled, columns = columns)\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "we_DGBw4xSGs",
        "outputId": "4180d1b4-3c69-416a-f623-3ca0c2ad204c"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          dji     eur    10yr_R  eu_money  us_money        ip      coal  \\\n",
              "152  0.713594  0.0000  0.937162  0.970345  0.774883  0.999048  1.000000   \n",
              "153  0.864333  0.0168  1.000000  0.985173  0.758867  1.000000  0.798909   \n",
              "154  0.934090  0.1212  0.875178  1.000000  0.776635  0.969028  0.904651   \n",
              "155  0.879876  0.1806  0.951379  0.983108  0.773647  0.915090  0.923227   \n",
              "156  0.915247  0.2124  0.846460  0.937688  0.756351  0.916460  0.527280   \n",
              "\n",
              "          nok   usd-nok  \n",
              "152  1.000000  0.000000  \n",
              "153  0.911525  0.044705  \n",
              "154  0.814764  0.098784  \n",
              "155  0.807364  0.103168  \n",
              "156  0.840742  0.083685  "
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eaeacdff-c450-4648-bbdc-4646f3dbc49e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eaeacdff-c450-4648-bbdc-4646f3dbc49e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.loc[:, df.columns == target][lag:]\n",
        "x = df.loc[:, df.columns != target][:-lag]\n",
        "test = df.loc[:, df.columns != target][-(lag + WINDOW_SIZE):]\n",
        "\n",
        "rng = pd.date_range('2010-07-01', periods = len(y), freq = 'M')\n",
        "y.index = rng\n",
        "x.index = rng\n",
        "\n",
        "df = pd.concat([y,x], axis = 1)\n",
        "\n",
        "plt.figure(figsize = (16,9))\n",
        "sns.lineplot(y=df[target], x = df.index)\n",
        "plt.xlabel('date')\n",
        "plt.ylabel('index')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "QGTljlT8yBnf",
        "outputId": "8f64a028-72f7-4b9c-a629-ddeed559f3a5"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'index')"
            ]
          },
          "metadata": {},
          "execution_count": 278
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_train = int(len(df) * 0.85)\n",
        "train = df\n",
        "n_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyMfiTpc0HGu",
        "outputId": "6d998ac2-07c9-4fa0-9a4b-ccf896db68fb"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "128"
            ]
          },
          "metadata": {},
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "feature_cols = x.columns\n",
        "label_cols = y.columns\n",
        "\n",
        "train_feature = x\n",
        "train_label = y\n",
        "\n",
        "train_feature, train_label = make_dataset(train_feature, train_label, 6)\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(train_feature, train_label, test_size = len(test))\n",
        "x_train.shape, x_valid.shape, y_train.shape, y_valid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3Zb8fPF0SED",
        "outputId": "64008f3c-5e30-410c-9070-a7e2cf010c1c"
      },
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((133, 6, 8), (12, 6, 8), (133, 1), (12, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4k2QCQRQ2NnR",
        "outputId": "a12025db-6768-4355-f422-30ab4249a4f0"
      },
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCEFCHGBlece",
        "outputId": "5ff7b6ae-16aa-43fe-c8bc-1ea5fb0853fa"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(151, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_feature = test\n",
        "test_label = pd.DataFrame([np.nan] * len(test_feature))\n",
        "\n",
        "test_feature.shape, test_label.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nKr7ZMz6nFD",
        "outputId": "72583690-2e73-49ab-f654-e9816c2ad889"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12, 8), (12, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_feature, test_label = make_dataset(test_feature, test_label, 6)\n",
        "test_feature.shape, test_label.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1gbQJcg6zrJ",
        "outputId": "d895fa38-c71f-4289-e8f5-a93879439369"
      },
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6, 6, 8), (6, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model**"
      ],
      "metadata": {
        "id": "XdjNW95o7Ecc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Conv1D, Lambda,Dropout, Flatten\n",
        "from tensorflow.keras.losses import Huber\n",
        "from tensorflow.keras.optimizers import Adam, SGD\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=256, return_sequences=True, activation='tanh', input_shape = (train_feature.shape[1], train_feature.shape[2])))\n",
        "model.add(LSTM(units=64, return_sequences=True, activation='tanh'))\n",
        "model.add(LSTM(units=32, activation = 'tanh'))\n",
        "model.add(Dense(units=1))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkxwf8l969Jj",
        "outputId": "6fcbfbc0-70d2-4350-ef7b-2b996d694987"
      },
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_33 (LSTM)              (None, 6, 256)            271360    \n",
            "                                                                 \n",
            " lstm_34 (LSTM)              (None, 6, 64)             82176     \n",
            "                                                                 \n",
            " lstm_35 (LSTM)              (None, 32)                12416     \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 365,985\n",
            "Trainable params: 365,985\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using Huber() of which performance is good for Sequence\n",
        "\n",
        "loss = Huber()\n",
        "optimizer = tf.keras.optimizers.SGD(momentum = 0.9)\n",
        "model.compile(loss=Huber(), optimizer = optimizer, metrics = ['mae'])"
      ],
      "metadata": {
        "id": "04XI0AJf7BmR"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stop learning if val_loss is not improved in ten epochs with earlystopping\n",
        "\n",
        "earlystopping = EarlyStopping(monitor = 'val_loss', patience = 10)\n",
        "\n",
        "# Checkpointer created on val_loss\n",
        "\n",
        "filename = os.path.join('tmp', 'checkpointer.ckpt')\n",
        "checkpoint = ModelCheckpoint(filename, \n",
        "                             save_weights_only = True,\n",
        "                             save_best_only = True,\n",
        "                             monitor = 'val_loss',\n",
        "                             verbose = 1)\n"
      ],
      "metadata": {
        "id": "G-JFDDESYGXb"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    validation_data = (x_valid, y_valid),\n",
        "                    epochs = 1000,\n",
        "                    callbacks = [checkpoint, earlystopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OkI5MixNYzvl",
        "outputId": "78aed48c-3ccc-468e-8864-365eb05d2b8e"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.1226 - mae: 0.4155\n",
            "Epoch 1: val_loss improved from inf to 0.08698, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 11s 440ms/step - loss: 0.1250 - mae: 0.4207 - val_loss: 0.0870 - val_mae: 0.3322\n",
            "Epoch 2/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0666 - mae: 0.2802\n",
            "Epoch 2: val_loss improved from 0.08698 to 0.03488, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0666 - mae: 0.2802 - val_loss: 0.0349 - val_mae: 0.2140\n",
            "Epoch 3/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0370 - mae: 0.2373\n",
            "Epoch 3: val_loss improved from 0.03488 to 0.03372, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 61ms/step - loss: 0.0370 - mae: 0.2373 - val_loss: 0.0337 - val_mae: 0.2270\n",
            "Epoch 4/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0434 - mae: 0.2558\n",
            "Epoch 4: val_loss did not improve from 0.03372\n",
            "5/5 [==============================] - 0s 52ms/step - loss: 0.0434 - mae: 0.2558 - val_loss: 0.0382 - val_mae: 0.2351\n",
            "Epoch 5/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0438 - mae: 0.2552\n",
            "Epoch 5: val_loss improved from 0.03372 to 0.03255, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 59ms/step - loss: 0.0438 - mae: 0.2552 - val_loss: 0.0325 - val_mae: 0.2232\n",
            "Epoch 6/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0369 - mae: 0.2397\n",
            "Epoch 6: val_loss improved from 0.03255 to 0.02821, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 66ms/step - loss: 0.0369 - mae: 0.2397 - val_loss: 0.0282 - val_mae: 0.2085\n",
            "Epoch 7/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0326 - mae: 0.2257\n",
            "Epoch 7: val_loss did not improve from 0.02821\n",
            "5/5 [==============================] - 0s 54ms/step - loss: 0.0327 - mae: 0.2255 - val_loss: 0.0285 - val_mae: 0.2018\n",
            "Epoch 8/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0325 - mae: 0.2194\n",
            "Epoch 8: val_loss did not improve from 0.02821\n",
            "5/5 [==============================] - 0s 52ms/step - loss: 0.0325 - mae: 0.2194 - val_loss: 0.0288 - val_mae: 0.1995\n",
            "Epoch 9/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0325 - mae: 0.2182\n",
            "Epoch 9: val_loss improved from 0.02821 to 0.02713, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 64ms/step - loss: 0.0319 - mae: 0.2158 - val_loss: 0.0271 - val_mae: 0.1967\n",
            "Epoch 10/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0306 - mae: 0.2150\n",
            "Epoch 10: val_loss improved from 0.02713 to 0.02541, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 62ms/step - loss: 0.0306 - mae: 0.2150 - val_loss: 0.0254 - val_mae: 0.1958\n",
            "Epoch 11/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0295 - mae: 0.2139\n",
            "Epoch 11: val_loss improved from 0.02541 to 0.02456, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 59ms/step - loss: 0.0295 - mae: 0.2139 - val_loss: 0.0246 - val_mae: 0.1961\n",
            "Epoch 12/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0283 - mae: 0.2105\n",
            "Epoch 12: val_loss improved from 0.02456 to 0.02383, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0286 - mae: 0.2114 - val_loss: 0.0238 - val_mae: 0.1926\n",
            "Epoch 13/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0278 - mae: 0.2083\n",
            "Epoch 13: val_loss improved from 0.02383 to 0.02305, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 64ms/step - loss: 0.0277 - mae: 0.2080 - val_loss: 0.0231 - val_mae: 0.1893\n",
            "Epoch 14/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0268 - mae: 0.2040\n",
            "Epoch 14: val_loss improved from 0.02305 to 0.02238, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 55ms/step - loss: 0.0268 - mae: 0.2040 - val_loss: 0.0224 - val_mae: 0.1849\n",
            "Epoch 15/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0259 - mae: 0.1991\n",
            "Epoch 15: val_loss improved from 0.02238 to 0.02190, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0259 - mae: 0.1991 - val_loss: 0.0219 - val_mae: 0.1796\n",
            "Epoch 16/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0258 - mae: 0.1967\n",
            "Epoch 16: val_loss improved from 0.02190 to 0.02136, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 61ms/step - loss: 0.0253 - mae: 0.1946 - val_loss: 0.0214 - val_mae: 0.1751\n",
            "Epoch 17/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0245 - mae: 0.1900\n",
            "Epoch 17: val_loss improved from 0.02136 to 0.02033, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 62ms/step - loss: 0.0245 - mae: 0.1900 - val_loss: 0.0203 - val_mae: 0.1725\n",
            "Epoch 18/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0233 - mae: 0.1867\n",
            "Epoch 18: val_loss improved from 0.02033 to 0.01899, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0233 - mae: 0.1867 - val_loss: 0.0190 - val_mae: 0.1716\n",
            "Epoch 19/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0221 - mae: 0.1839\n",
            "Epoch 19: val_loss improved from 0.01899 to 0.01799, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0221 - mae: 0.1839 - val_loss: 0.0180 - val_mae: 0.1693\n",
            "Epoch 20/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0212 - mae: 0.1813\n",
            "Epoch 20: val_loss improved from 0.01799 to 0.01704, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 63ms/step - loss: 0.0211 - mae: 0.1809 - val_loss: 0.0170 - val_mae: 0.1678\n",
            "Epoch 21/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0205 - mae: 0.1795\n",
            "Epoch 21: val_loss improved from 0.01704 to 0.01614, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 62ms/step - loss: 0.0201 - mae: 0.1775 - val_loss: 0.0161 - val_mae: 0.1640\n",
            "Epoch 22/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0191 - mae: 0.1724\n",
            "Epoch 22: val_loss improved from 0.01614 to 0.01527, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 60ms/step - loss: 0.0191 - mae: 0.1724 - val_loss: 0.0153 - val_mae: 0.1583\n",
            "Epoch 23/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0182 - mae: 0.1666\n",
            "Epoch 23: val_loss improved from 0.01527 to 0.01455, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 102ms/step - loss: 0.0181 - mae: 0.1664 - val_loss: 0.0146 - val_mae: 0.1519\n",
            "Epoch 24/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0166 - mae: 0.1574\n",
            "Epoch 24: val_loss improved from 0.01455 to 0.01359, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 97ms/step - loss: 0.0171 - mae: 0.1594 - val_loss: 0.0136 - val_mae: 0.1476\n",
            "Epoch 25/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0164 - mae: 0.1574\n",
            "Epoch 25: val_loss improved from 0.01359 to 0.01254, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 101ms/step - loss: 0.0160 - mae: 0.1548 - val_loss: 0.0125 - val_mae: 0.1446\n",
            "Epoch 26/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0151 - mae: 0.1506\n",
            "Epoch 26: val_loss improved from 0.01254 to 0.01173, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 102ms/step - loss: 0.0149 - mae: 0.1499 - val_loss: 0.0117 - val_mae: 0.1401\n",
            "Epoch 27/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0140 - mae: 0.1440\n",
            "Epoch 27: val_loss improved from 0.01173 to 0.01107, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 97ms/step - loss: 0.0140 - mae: 0.1440 - val_loss: 0.0111 - val_mae: 0.1338\n",
            "Epoch 28/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0134 - mae: 0.1388\n",
            "Epoch 28: val_loss improved from 0.01107 to 0.01034, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 96ms/step - loss: 0.0131 - mae: 0.1373 - val_loss: 0.0103 - val_mae: 0.1286\n",
            "Epoch 29/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0123 - mae: 0.1318\n",
            "Epoch 29: val_loss improved from 0.01034 to 0.00955, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 97ms/step - loss: 0.0122 - mae: 0.1315 - val_loss: 0.0095 - val_mae: 0.1254\n",
            "Epoch 30/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0111 - mae: 0.1256\n",
            "Epoch 30: val_loss improved from 0.00955 to 0.00888, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 97ms/step - loss: 0.0113 - mae: 0.1270 - val_loss: 0.0089 - val_mae: 0.1214\n",
            "Epoch 31/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0104 - mae: 0.1215\n",
            "Epoch 31: val_loss improved from 0.00888 to 0.00827, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 104ms/step - loss: 0.0104 - mae: 0.1215 - val_loss: 0.0083 - val_mae: 0.1168\n",
            "Epoch 32/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0096 - mae: 0.1159\n",
            "Epoch 32: val_loss improved from 0.00827 to 0.00774, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 113ms/step - loss: 0.0096 - mae: 0.1159 - val_loss: 0.0077 - val_mae: 0.1121\n",
            "Epoch 33/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0089 - mae: 0.1106\n",
            "Epoch 33: val_loss improved from 0.00774 to 0.00725, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 105ms/step - loss: 0.0089 - mae: 0.1106 - val_loss: 0.0073 - val_mae: 0.1069\n",
            "Epoch 34/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0081 - mae: 0.1039\n",
            "Epoch 34: val_loss improved from 0.00725 to 0.00679, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 100ms/step - loss: 0.0082 - mae: 0.1044 - val_loss: 0.0068 - val_mae: 0.1011\n",
            "Epoch 35/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0077 - mae: 0.0986\n",
            "Epoch 35: val_loss improved from 0.00679 to 0.00642, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 113ms/step - loss: 0.0077 - mae: 0.0986 - val_loss: 0.0064 - val_mae: 0.0990\n",
            "Epoch 36/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0072 - mae: 0.0936\n",
            "Epoch 36: val_loss improved from 0.00642 to 0.00617, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 66ms/step - loss: 0.0071 - mae: 0.0935 - val_loss: 0.0062 - val_mae: 0.0960\n",
            "Epoch 37/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0067 - mae: 0.0900\n",
            "Epoch 37: val_loss improved from 0.00617 to 0.00603, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 58ms/step - loss: 0.0067 - mae: 0.0900 - val_loss: 0.0060 - val_mae: 0.0928\n",
            "Epoch 38/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0065 - mae: 0.0883\n",
            "Epoch 38: val_loss improved from 0.00603 to 0.00581, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 66ms/step - loss: 0.0063 - mae: 0.0862 - val_loss: 0.0058 - val_mae: 0.0917\n",
            "Epoch 39/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0060 - mae: 0.0834\n",
            "Epoch 39: val_loss improved from 0.00581 to 0.00567, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 66ms/step - loss: 0.0060 - mae: 0.0838 - val_loss: 0.0057 - val_mae: 0.0903\n",
            "Epoch 40/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0058 - mae: 0.0820\n",
            "Epoch 40: val_loss improved from 0.00567 to 0.00557, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 59ms/step - loss: 0.0058 - mae: 0.0820 - val_loss: 0.0056 - val_mae: 0.0891\n",
            "Epoch 41/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0056 - mae: 0.0807\n",
            "Epoch 41: val_loss improved from 0.00557 to 0.00551, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 62ms/step - loss: 0.0056 - mae: 0.0807 - val_loss: 0.0055 - val_mae: 0.0878\n",
            "Epoch 42/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0055 - mae: 0.0803\n",
            "Epoch 42: val_loss improved from 0.00551 to 0.00551, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 64ms/step - loss: 0.0054 - mae: 0.0799 - val_loss: 0.0055 - val_mae: 0.0861\n",
            "Epoch 43/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0053 - mae: 0.0785\n",
            "Epoch 43: val_loss did not improve from 0.00551\n",
            "5/5 [==============================] - 0s 55ms/step - loss: 0.0053 - mae: 0.0785 - val_loss: 0.0057 - val_mae: 0.0833\n",
            "Epoch 44/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0052 - mae: 0.0770\n",
            "Epoch 44: val_loss did not improve from 0.00551\n",
            "5/5 [==============================] - 0s 54ms/step - loss: 0.0052 - mae: 0.0778 - val_loss: 0.0056 - val_mae: 0.0824\n",
            "Epoch 45/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0053 - mae: 0.0792\n",
            "Epoch 45: val_loss improved from 0.00551 to 0.00549, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 70ms/step - loss: 0.0051 - mae: 0.0772 - val_loss: 0.0055 - val_mae: 0.0828\n",
            "Epoch 46/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0051 - mae: 0.0774\n",
            "Epoch 46: val_loss improved from 0.00549 to 0.00545, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 0s 98ms/step - loss: 0.0050 - mae: 0.0771 - val_loss: 0.0055 - val_mae: 0.0825\n",
            "Epoch 47/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0051 - mae: 0.0774\n",
            "Epoch 47: val_loss did not improve from 0.00545\n",
            "5/5 [==============================] - 0s 84ms/step - loss: 0.0050 - mae: 0.0768 - val_loss: 0.0055 - val_mae: 0.0811\n",
            "Epoch 48/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0048 - mae: 0.0740\n",
            "Epoch 48: val_loss did not improve from 0.00545\n",
            "5/5 [==============================] - 0s 90ms/step - loss: 0.0049 - mae: 0.0759 - val_loss: 0.0057 - val_mae: 0.0809\n",
            "Epoch 49/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0049 - mae: 0.0758\n",
            "Epoch 49: val_loss did not improve from 0.00545\n",
            "5/5 [==============================] - 0s 90ms/step - loss: 0.0049 - mae: 0.0758 - val_loss: 0.0056 - val_mae: 0.0809\n",
            "Epoch 50/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0048 - mae: 0.0757\n",
            "Epoch 50: val_loss did not improve from 0.00545\n",
            "5/5 [==============================] - 0s 90ms/step - loss: 0.0048 - mae: 0.0755 - val_loss: 0.0055 - val_mae: 0.0809\n",
            "Epoch 51/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0048 - mae: 0.0760\n",
            "Epoch 51: val_loss improved from 0.00545 to 0.00540, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 108ms/step - loss: 0.0048 - mae: 0.0760 - val_loss: 0.0054 - val_mae: 0.0809\n",
            "Epoch 52/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0050 - mae: 0.0789\n",
            "Epoch 52: val_loss improved from 0.00540 to 0.00537, saving model to tmp/checkpointer.ckpt\n",
            "5/5 [==============================] - 1s 110ms/step - loss: 0.0048 - mae: 0.0768 - val_loss: 0.0054 - val_mae: 0.0809\n",
            "Epoch 53/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0049 - mae: 0.0770\n",
            "Epoch 53: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 89ms/step - loss: 0.0048 - mae: 0.0762 - val_loss: 0.0054 - val_mae: 0.0807\n",
            "Epoch 54/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0048 - mae: 0.0757\n",
            "Epoch 54: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 92ms/step - loss: 0.0047 - mae: 0.0750 - val_loss: 0.0056 - val_mae: 0.0804\n",
            "Epoch 55/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0047 - mae: 0.0744\n",
            "Epoch 55: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 99ms/step - loss: 0.0047 - mae: 0.0744 - val_loss: 0.0057 - val_mae: 0.0803\n",
            "Epoch 56/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0047 - mae: 0.0746\n",
            "Epoch 56: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 85ms/step - loss: 0.0047 - mae: 0.0745 - val_loss: 0.0055 - val_mae: 0.0803\n",
            "Epoch 57/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0046 - mae: 0.0743\n",
            "Epoch 57: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 95ms/step - loss: 0.0046 - mae: 0.0743 - val_loss: 0.0055 - val_mae: 0.0802\n",
            "Epoch 58/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0045 - mae: 0.0735\n",
            "Epoch 58: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 92ms/step - loss: 0.0046 - mae: 0.0739 - val_loss: 0.0055 - val_mae: 0.0801\n",
            "Epoch 59/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0046 - mae: 0.0737\n",
            "Epoch 59: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 100ms/step - loss: 0.0046 - mae: 0.0737 - val_loss: 0.0054 - val_mae: 0.0801\n",
            "Epoch 60/1000\n",
            "4/5 [=======================>......] - ETA: 0s - loss: 0.0046 - mae: 0.0742\n",
            "Epoch 60: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 58ms/step - loss: 0.0045 - mae: 0.0737 - val_loss: 0.0055 - val_mae: 0.0800\n",
            "Epoch 61/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0045 - mae: 0.0733\n",
            "Epoch 61: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 53ms/step - loss: 0.0045 - mae: 0.0733 - val_loss: 0.0055 - val_mae: 0.0798\n",
            "Epoch 62/1000\n",
            "5/5 [==============================] - ETA: 0s - loss: 0.0045 - mae: 0.0731\n",
            "Epoch 62: val_loss did not improve from 0.00537\n",
            "5/5 [==============================] - 0s 91ms/step - loss: 0.0045 - mae: 0.0731 - val_loss: 0.0054 - val_mae: 0.0798\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_vloss = history.history['val_loss']\n",
        "y_loss = history.history['loss']\n",
        "\n",
        "x_len = np.arange(len(y_loss))\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(x_len, y_vloss, marker = '.', c = 'red', label = 'Validation set loss')\n",
        "plt.plot(x_len, y_loss, marker = '.', c='blue', label = 'Train set loss')\n",
        "\n",
        "plt.legend(loc = 'upper right')\n",
        "plt.grid()\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "igKI_VhvZG0o",
        "outputId": "a9cd86ce-5e34-427f-aee4-3226eaa7621d"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights(filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJRpHOTxZ_MR",
        "outputId": "720c5dd0-5078-426a-95a5-8cc91586c485"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7fb205c9b880>"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(test_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iddw2pjtaIY8",
        "outputId": "7b6391bd-a357-4a2b-df67-e8cc3ddad089"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "empty = np.empty((len(y)-1,1))\n",
        "empty[:] = np.nan"
      ],
      "metadata": {
        "id": "9uRb0n8DaL0E"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(scaler.inverse_transform(scaled), columns = columns)\n",
        "y = df.loc[:, df.columns == target][lag:]\n",
        "y.index = rng"
      ],
      "metadata": {
        "id": "O5qlgYYTgSwU"
      },
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = pred * scale + mini\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvrD9D_iggfP",
        "outputId": "30b2b564-a6d4-49b3-abd5-d8dc3cb921e6"
      },
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[33385.805],\n",
              "       [33535.914],\n",
              "       [33788.97 ],\n",
              "       [33938.11 ],\n",
              "       [34136.42 ],\n",
              "       [34129.6  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1=pd.DataFrame(pred)\n",
        "pred1.columns = ['dji_pred']\n",
        "pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "UHQyJDKb3iy6",
        "outputId": "e516572a-9742-459b-b46f-e77d791eb930"
      },
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       dji_pred\n",
              "0  33385.804688\n",
              "1  33535.914062\n",
              "2  33788.968750\n",
              "3  33938.109375\n",
              "4  34136.421875\n",
              "5  34129.601562"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e33dc6c-6ff8-4951-90b8-afa82b883eb8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dji_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33385.804688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33535.914062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33788.968750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33938.109375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34136.421875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34129.601562</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e33dc6c-6ff8-4951-90b8-afa82b883eb8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e33dc6c-6ff8-4951-90b8-afa82b883eb8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e33dc6c-6ff8-4951-90b8-afa82b883eb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual1 = y.iloc[-6:]\n",
        "actual1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "PQUB0kbW3nef",
        "outputId": "7856e317-7031-4f41-f52c-541a5d29740f"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 dji\n",
              "2022-08-31  31511.09\n",
              "2022-09-30  28730.12\n",
              "2022-10-31  32734.40\n",
              "2022-11-30  34587.46\n",
              "2022-12-31  33147.28\n",
              "2023-01-31  34086.89"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afac2b8f-3b1b-474d-8682-a79d3073fba8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dji</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-08-31</th>\n",
              "      <td>31511.09</td>\n",
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              "    <tr>\n",
              "      <th>2022-09-30</th>\n",
              "      <td>28730.12</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-31</th>\n",
              "      <td>32734.40</td>\n",
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              "    <tr>\n",
              "      <th>2022-11-30</th>\n",
              "      <td>34587.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>33147.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-31</th>\n",
              "      <td>34086.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afac2b8f-3b1b-474d-8682-a79d3073fba8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-afac2b8f-3b1b-474d-8682-a79d3073fba8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-afac2b8f-3b1b-474d-8682-a79d3073fba8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mse_arima = mean_squared_error(actual1['dji'].values, pred1['dji_pred'].values, squared=False)\n",
        "print('MSE: '+str(mse_arima))\n",
        "mae_arima = mean_absolute_error(actual1['dji'].values, pred1['dji_pred'].values)\n",
        "print('MAE: '+str(mae_arima))\n",
        "mape_arima = np.mean(np.abs(actual1['dji'].values - pred1['dji_pred'].values)/np.abs(actual1['dji'].values))\n",
        "print('MAPE: '+str(mape_arima)) # 4.5% of absolute gap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHXfYwGJitVX",
        "outputId": "746a7da5-02ff-4292-ab4e-fcf743079ca3"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 2203.1874311174174\n",
            "MAE: 1569.380260416665\n",
            "MAPE: 0.05147524072407283\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S_wWrij4yoxd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}