PredStockPrice_LSTM.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# I. Install and import libraries"
      ],
      "metadata": {
        "id": "Z6Uye4drlJ9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "549ZPQ5T74aE",
        "outputId": "984623d4-2d40-4e89-8c44-f9bf7b58316c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wandb\n",
            "  Downloading wandb-0.16.4-py3-none-any.whl (2.2 MB)\n",
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            "  Downloading sentry_sdk-1.40.6-py2.py3-none-any.whl (258 kB)\n",
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            "\u001b[?25hCollecting docker-pycreds>=0.4.0 (from wandb)\n",
            "  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
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            "  Downloading setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
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            "Collecting gitdb<5,>=4.0.1 (from GitPython!=3.1.29,>=1.0.0->wandb)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.2.2)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n",
            "Successfully installed GitPython-3.1.42 docker-pycreds-0.4.0 gitdb-4.0.11 sentry-sdk-1.40.6 setproctitle-1.3.3 smmap-5.0.1 wandb-0.16.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "slV0OilVkAk9"
      },
      "outputs": [],
      "source": [
        "#Importing the Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib. pyplot as plt\n",
        "import matplotlib\n",
        "from sklearn. preprocessing import MinMaxScaler\n",
        "from keras. layers import LSTM, Dense, Dropout\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_percentage_error\n",
        "import matplotlib. dates as mandates\n",
        "\n",
        "from sklearn import linear_model\n",
        "from keras. models import Sequential\n",
        "from keras. layers import Dense\n",
        "import keras. backend as K\n",
        "from keras. callbacks import EarlyStopping\n",
        "from keras. optimizers import Adam\n",
        "from keras. models import load_model\n",
        "from keras. layers import LSTM\n",
        "from keras.utils import plot_model\n",
        "\n",
        "import wandb\n",
        "from wandb.keras import WandbCallback, WandbMetricsLogger"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# II. Prepare data"
      ],
      "metadata": {
        "id": "OX0ILK-gnQ1g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Download & Read data"
      ],
      "metadata": {
        "id": "VhxJ9SNJnV6a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/ntt2125/Finpros.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YrtYwQ9nVOf",
        "outputId": "9cfd5446-fe3a-4f97-81a6-7f5e80a86a04"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Finpros'...\n",
            "remote: Enumerating objects: 12, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 12 (delta 1), reused 8 (delta 1), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (12/12), 3.05 MiB | 4.80 MiB/s, done.\n",
            "Resolving deltas: 100% (1/1), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_name='MSN'\n",
        "data_path = f'/content/Finpros/data/{data_name}.csv'\n",
        "\n",
        "df = pd.read_csv(data_path, na_values=['null'], index_col='Date/Time', parse_dates=True, infer_datetime_format=True)\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "2JQnyi1Jnuy-",
        "outputId": "03f1e4ad-e577-45d4-f6e5-fcb569dd6a74"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Ticker  Open  High   Low  Close  Volume  Open Interest\n",
              "Date/Time                                                                 \n",
              "2017-12-25 09:15:00    MSN  73.1  73.1  73.1   73.1    4210              0\n",
              "2017-12-25 09:16:00    MSN  73.0  73.0  73.0   73.0    5000              0\n",
              "2017-12-25 09:18:00    MSN  73.5  73.5  73.5   73.5     210              0\n",
              "2017-12-25 09:20:00    MSN  73.2  73.5  73.1   73.1    2050              0\n",
              "2017-12-25 09:21:00    MSN  73.0  73.0  73.0   73.0    1380              0\n",
              "2017-12-25 09:22:00    MSN  73.0  73.1  73.0   73.1    1600              0\n",
              "2017-12-25 09:23:00    MSN  73.1  73.1  73.1   73.1     910              0\n",
              "2017-12-25 09:27:00    MSN  73.3  73.3  73.3   73.3     120              0\n",
              "2017-12-25 09:30:00    MSN  73.5  73.5  73.5   73.5     520              0\n",
              "2017-12-25 09:36:00    MSN  73.3  73.3  73.3   73.3     780              0"
            ],
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              "      <th>2017-12-25 09:15:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.1</td>\n",
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              "      <td>MSN</td>\n",
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              "      <th>2017-12-25 09:18:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.5</td>\n",
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              "      <th>2017-12-25 09:20:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.2</td>\n",
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              "      <td>73.1</td>\n",
              "      <td>73.1</td>\n",
              "      <td>2050</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:21:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1380</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:22:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.1</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.1</td>\n",
              "      <td>1600</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:23:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.1</td>\n",
              "      <td>73.1</td>\n",
              "      <td>73.1</td>\n",
              "      <td>73.1</td>\n",
              "      <td>910</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:27:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:30:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.5</td>\n",
              "      <td>520</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:36:00</th>\n",
              "      <td>MSN</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>73.3</td>\n",
              "      <td>780</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1098bf6-af51-4a35-b971-debdf77b969d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1098bf6-af51-4a35-b971-debdf77b969d 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-a1098bf6-af51-4a35-b971-debdf77b969d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8646e0af-e8c7-44fc-a3bb-db5f5c9ab710\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8646e0af-e8c7-44fc-a3bb-db5f5c9ab710')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8646e0af-e8c7-44fc-a3bb-db5f5c9ab710 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking null val\n",
        "print(f\"Dataframe shape: {df.shape}\")\n",
        "print('Null value present: ', df.isnull().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VUQEkxvovvp",
        "outputId": "f462027c-f86c-4902-cfa6-5071659425ec"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe shape: (135354, 7)\n",
            "Null value present:  False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Close'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "snNmtBNUpaQh",
        "outputId": "2019a1d3-13ba-4af7-de16-65bf1a6f4700"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date/Time'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Setting target and select feature"
      ],
      "metadata": {
        "id": "gf_MS7-ArkPZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Target Variable\n",
        "output_var = pd.DataFrame(df['Close'])\n",
        "\n",
        "# Select the features\n",
        "features = ['Open', 'High', 'Low', 'Volume']\n"
      ],
      "metadata": {
        "id": "RdbKOhobpsPl"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Scaling"
      ],
      "metadata": {
        "id": "InAtCN9MsM7k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "feature_transform = scaler.fit_transform(df[features])\n",
        "feature_transform = pd.DataFrame(columns=features, data=feature_transform, index=df.index)\n",
        "feature_transform.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "beAbl0LbsRDw",
        "outputId": "68fac230-5383-4117-8cba-f5c3de7429c7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Open      High       Low    Volume\n",
              "Date/Time                                                  \n",
              "2017-12-25 09:15:00  0.373072  0.372028  0.374474  0.001272\n",
              "2017-12-25 09:16:00  0.371669  0.370629  0.373072  0.001512\n",
              "2017-12-25 09:18:00  0.378682  0.377622  0.380084  0.000061\n",
              "2017-12-25 09:20:00  0.374474  0.377622  0.374474  0.000618\n",
              "2017-12-25 09:21:00  0.371669  0.370629  0.373072  0.000415\n",
              "2017-12-25 09:22:00  0.371669  0.372028  0.373072  0.000482\n",
              "2017-12-25 09:23:00  0.373072  0.372028  0.374474  0.000273\n",
              "2017-12-25 09:27:00  0.375877  0.374825  0.377279  0.000033\n",
              "2017-12-25 09:30:00  0.378682  0.377622  0.380084  0.000155\n",
              "2017-12-25 09:36:00  0.375877  0.374825  0.377279  0.000233"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3518c0e8-7297-41fd-807d-b77f22617d4d\" 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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date/Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:15:00</th>\n",
              "      <td>0.373072</td>\n",
              "      <td>0.372028</td>\n",
              "      <td>0.374474</td>\n",
              "      <td>0.001272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:16:00</th>\n",
              "      <td>0.371669</td>\n",
              "      <td>0.370629</td>\n",
              "      <td>0.373072</td>\n",
              "      <td>0.001512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:18:00</th>\n",
              "      <td>0.378682</td>\n",
              "      <td>0.377622</td>\n",
              "      <td>0.380084</td>\n",
              "      <td>0.000061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:20:00</th>\n",
              "      <td>0.374474</td>\n",
              "      <td>0.377622</td>\n",
              "      <td>0.374474</td>\n",
              "      <td>0.000618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:21:00</th>\n",
              "      <td>0.371669</td>\n",
              "      <td>0.370629</td>\n",
              "      <td>0.373072</td>\n",
              "      <td>0.000415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:22:00</th>\n",
              "      <td>0.371669</td>\n",
              "      <td>0.372028</td>\n",
              "      <td>0.373072</td>\n",
              "      <td>0.000482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:23:00</th>\n",
              "      <td>0.373072</td>\n",
              "      <td>0.372028</td>\n",
              "      <td>0.374474</td>\n",
              "      <td>0.000273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:27:00</th>\n",
              "      <td>0.375877</td>\n",
              "      <td>0.374825</td>\n",
              "      <td>0.377279</td>\n",
              "      <td>0.000033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:30:00</th>\n",
              "      <td>0.378682</td>\n",
              "      <td>0.377622</td>\n",
              "      <td>0.380084</td>\n",
              "      <td>0.000155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-25 09:36:00</th>\n",
              "      <td>0.375877</td>\n",
              "      <td>0.374825</td>\n",
              "      <td>0.377279</td>\n",
              "      <td>0.000233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3518c0e8-7297-41fd-807d-b77f22617d4d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3518c0e8-7297-41fd-807d-b77f22617d4d 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-3518c0e8-7297-41fd-807d-b77f22617d4d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eab75592-3dab-42a1-8272-bd5961a38437\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eab75592-3dab-42a1-8272-bd5961a38437')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eab75592-3dab-42a1-8272-bd5961a38437 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_transform"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Split data to train, val, test"
      ],
      "metadata": {
        "id": "eCgAfcbntgc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "timesplit = TimeSeriesSplit(n_splits=10)\n",
        "for train_index, test_index in timesplit.split(feature_transform):\n",
        "    X_train, X_test = feature_transform[:len(train_index)], feature_transform[len(train_index): (len(train_index)+len(test_index))]\n",
        "    y_train, y_test = output_var[:len(train_index)].values.ravel(), output_var[len(train_index): (len(train_index)+len(test_index))].values.ravel()"
      ],
      "metadata": {
        "id": "Vtes5uu-tmSR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Data Processing"
      ],
      "metadata": {
        "id": "Bpia3j94u7lI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_X = np.array(X_train)\n",
        "test_X = np.array(X_test)\n",
        "X_train = train_X.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
        "X_test = test_X.reshape(X_test.shape[0], 1, X_test.shape[1])\n"
      ],
      "metadata": {
        "id": "U0uHYtPZu_3L"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHw3RZnl0GUW",
        "outputId": "cac87503-fd3a-417f-fc01-470210cd9b34"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(123050, 1, 4)\n",
            "(12304, 1, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# III. Config"
      ],
      "metadata": {
        "id": "fQbtRh8I_RVN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wandb login\n",
        "# 4fd3a87393d5814c8c017631bd62012229e273e1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFVmcNA__XND",
        "outputId": "4beb8802-eba2-4627-f24d-b8951a135dc1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = {\n",
        "    'epochs': 200,\n",
        "    'batch_size': 256,\n",
        "    'activation': 'relu',\n",
        "    'loss': 'mean_squared_error',\n",
        "    'optimizer': 'adam',\n",
        "    'metics': ['mape']\n",
        "}\n",
        "\n",
        "wandb.init(\n",
        "    project='Finpros',\n",
        "    name=f'model LSTM on stock data {data_name}.csv',\n",
        "    config=config,\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "PGHAzCaB_ZdC",
        "outputId": "da4ed22b-f601-479c-f46d-542cca1bdf73"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnguyentienthong2125\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.4"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240307_045428-a1cbiuwi</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi' target=\"_blank\">model LSTM on stock data MSN.csv</a></strong> to <a href='https://wandb.ai/nguyentienthong2125/Finpros' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/nguyentienthong2125/Finpros' target=\"_blank\">https://wandb.ai/nguyentienthong2125/Finpros</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi' target=\"_blank\">https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7be113f93850>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# III. Buil model\n"
      ],
      "metadata": {
        "id": "rg_k0G3Hv16T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lstm = Sequential()\n",
        "lstm.add(LSTM(32, input_shape=(1, train_X.shape[1]), activation=config['activation'], return_sequences=False))\n",
        "lstm.add(Dense(1))\n",
        "lstm.compile(loss=config['loss'], optimizer=config['optimizer'], metrics=config['metics'])\n",
        "\n"
      ],
      "metadata": {
        "id": "fC13F5yiv5JV"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_X.shape[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJDhi1GPFYib",
        "outputId": "393fc4b7-0a12-418f-e591-5e6bca056c9b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## IV. Training"
      ],
      "metadata": {
        "id": "VGpAwBqhwaDG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history=lstm.fit(X_train, y_train,\n",
        "                 validation_data=(X_test, y_test),\n",
        "                 epochs=config['epochs'],\n",
        "                 batch_size=config['batch_size'],\n",
        "                 verbose=1,\n",
        "                 shuffle=False,\n",
        "                 callbacks=[WandbMetricsLogger()])\n",
        "wandb.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "9964642f00164c798240b93ef11c6884",
            "32122caadc834481a22c1d1d34e656ae",
            "d44591cb1bf042f9bedafc031d3a79f1",
            "84fda1efc12d43e4bc77a35a2e8af7e8",
            "23c671b06d08455f82c7e90d58faa693",
            "97879d2e50ee444b8a7b7178b68d925f",
            "a6b208071f134da5a082e04f3277d74b",
            "2a4262800df6406cba40ac9236a58155"
          ]
        },
        "id": "SbDOxhfOwfty",
        "outputId": "c5cae804-1b54-48af-b932-f15b226bda2c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "481/481 [==============================] - 5s 6ms/step - loss: 5516.1763 - mape: 96.9018 - val_loss: 5073.9507 - val_mape: 88.3626\n",
            "Epoch 2/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 2406.1501 - mape: 61.4420 - val_loss: 666.2842 - val_mape: 32.7761\n",
            "Epoch 3/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 215.0311 - mape: 19.9182 - val_loss: 36.8042 - val_mape: 7.0451\n",
            "Epoch 4/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 61.3451 - mape: 9.8733 - val_loss: 82.2972 - val_mape: 10.6237\n",
            "Epoch 5/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 52.0491 - mape: 8.6399 - val_loss: 80.7543 - val_mape: 10.4147\n",
            "Epoch 6/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 36.3792 - mape: 7.0147 - val_loss: 71.7556 - val_mape: 9.7360\n",
            "Epoch 7/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 23.8689 - mape: 5.4890 - val_loss: 59.4534 - val_mape: 8.8098\n",
            "Epoch 8/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 14.7560 - mape: 4.1352 - val_loss: 45.8752 - val_mape: 7.7207\n",
            "Epoch 9/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 8.4703 - mape: 2.9853 - val_loss: 33.1492 - val_mape: 6.5621\n",
            "Epoch 10/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 4.5790 - mape: 2.1028 - val_loss: 21.7131 - val_mape: 5.3180\n",
            "Epoch 11/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 2.3077 - mape: 1.4231 - val_loss: 13.0084 - val_mape: 4.1186\n",
            "Epoch 12/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 1.1326 - mape: 0.9551 - val_loss: 7.3089 - val_mape: 3.0776\n",
            "Epoch 13/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.6459 - mape: 0.7712 - val_loss: 3.8447 - val_mape: 2.2010\n",
            "Epoch 14/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.4458 - mape: 0.6687 - val_loss: 2.0741 - val_mape: 1.5629\n",
            "Epoch 15/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.3557 - mape: 0.5879 - val_loss: 1.2142 - val_mape: 1.1293\n",
            "Epoch 16/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.3023 - mape: 0.5202 - val_loss: 0.7727 - val_mape: 0.8382\n",
            "Epoch 17/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.2624 - mape: 0.4683 - val_loss: 0.5302 - val_mape: 0.6417\n",
            "Epoch 18/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.2308 - mape: 0.4301 - val_loss: 0.3982 - val_mape: 0.5028\n",
            "Epoch 19/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.2090 - mape: 0.4020 - val_loss: 0.3297 - val_mape: 0.4223\n",
            "Epoch 20/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.1929 - mape: 0.3798 - val_loss: 0.2886 - val_mape: 0.3903\n",
            "Epoch 21/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.1794 - mape: 0.3611 - val_loss: 0.2568 - val_mape: 0.3751\n",
            "Epoch 22/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.1664 - mape: 0.3439 - val_loss: 0.2282 - val_mape: 0.3623\n",
            "Epoch 23/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.1529 - mape: 0.3277 - val_loss: 0.2037 - val_mape: 0.3519\n",
            "Epoch 24/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.1399 - mape: 0.3124 - val_loss: 0.1821 - val_mape: 0.3443\n",
            "Epoch 25/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.1279 - mape: 0.2981 - val_loss: 0.1652 - val_mape: 0.3403\n",
            "Epoch 26/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.1167 - mape: 0.2844 - val_loss: 0.1505 - val_mape: 0.3354\n",
            "Epoch 27/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.1069 - mape: 0.2724 - val_loss: 0.1381 - val_mape: 0.3287\n",
            "Epoch 28/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0984 - mape: 0.2619 - val_loss: 0.1279 - val_mape: 0.3202\n",
            "Epoch 29/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0910 - mape: 0.2523 - val_loss: 0.1198 - val_mape: 0.3101\n",
            "Epoch 30/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0848 - mape: 0.2437 - val_loss: 0.1137 - val_mape: 0.2994\n",
            "Epoch 31/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0796 - mape: 0.2363 - val_loss: 0.1091 - val_mape: 0.2888\n",
            "Epoch 32/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0749 - mape: 0.2291 - val_loss: 0.1058 - val_mape: 0.2793\n",
            "Epoch 33/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0708 - mape: 0.2223 - val_loss: 0.1035 - val_mape: 0.2712\n",
            "Epoch 34/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0671 - mape: 0.2159 - val_loss: 0.1018 - val_mape: 0.2645\n",
            "Epoch 35/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0637 - mape: 0.2100 - val_loss: 0.1005 - val_mape: 0.2599\n",
            "Epoch 36/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0606 - mape: 0.2043 - val_loss: 0.0997 - val_mape: 0.2571\n",
            "Epoch 37/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0577 - mape: 0.1990 - val_loss: 0.0992 - val_mape: 0.2556\n",
            "Epoch 38/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0550 - mape: 0.1941 - val_loss: 0.0989 - val_mape: 0.2550\n",
            "Epoch 39/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0524 - mape: 0.1895 - val_loss: 0.0986 - val_mape: 0.2549\n",
            "Epoch 40/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0500 - mape: 0.1854 - val_loss: 0.0984 - val_mape: 0.2549\n",
            "Epoch 41/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0477 - mape: 0.1815 - val_loss: 0.0979 - val_mape: 0.2548\n",
            "Epoch 42/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0455 - mape: 0.1780 - val_loss: 0.0973 - val_mape: 0.2541\n",
            "Epoch 43/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0434 - mape: 0.1747 - val_loss: 0.0963 - val_mape: 0.2526\n",
            "Epoch 44/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0414 - mape: 0.1716 - val_loss: 0.0951 - val_mape: 0.2505\n",
            "Epoch 45/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0395 - mape: 0.1686 - val_loss: 0.0936 - val_mape: 0.2478\n",
            "Epoch 46/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0377 - mape: 0.1657 - val_loss: 0.0921 - val_mape: 0.2447\n",
            "Epoch 47/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0360 - mape: 0.1629 - val_loss: 0.0906 - val_mape: 0.2416\n",
            "Epoch 48/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0344 - mape: 0.1601 - val_loss: 0.0893 - val_mape: 0.2388\n",
            "Epoch 49/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0329 - mape: 0.1574 - val_loss: 0.0881 - val_mape: 0.2363\n",
            "Epoch 50/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0316 - mape: 0.1546 - val_loss: 0.0870 - val_mape: 0.2343\n",
            "Epoch 51/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0304 - mape: 0.1520 - val_loss: 0.0862 - val_mape: 0.2326\n",
            "Epoch 52/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0293 - mape: 0.1494 - val_loss: 0.0855 - val_mape: 0.2312\n",
            "Epoch 53/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0284 - mape: 0.1469 - val_loss: 0.0849 - val_mape: 0.2301\n",
            "Epoch 54/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0275 - mape: 0.1446 - val_loss: 0.0845 - val_mape: 0.2292\n",
            "Epoch 55/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0268 - mape: 0.1423 - val_loss: 0.0841 - val_mape: 0.2285\n",
            "Epoch 56/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0261 - mape: 0.1400 - val_loss: 0.0837 - val_mape: 0.2279\n",
            "Epoch 57/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0254 - mape: 0.1378 - val_loss: 0.0833 - val_mape: 0.2274\n",
            "Epoch 58/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0248 - mape: 0.1356 - val_loss: 0.0830 - val_mape: 0.2270\n",
            "Epoch 59/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0243 - mape: 0.1335 - val_loss: 0.0827 - val_mape: 0.2266\n",
            "Epoch 60/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0238 - mape: 0.1315 - val_loss: 0.0823 - val_mape: 0.2261\n",
            "Epoch 61/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0233 - mape: 0.1296 - val_loss: 0.0820 - val_mape: 0.2257\n",
            "Epoch 62/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0228 - mape: 0.1277 - val_loss: 0.0817 - val_mape: 0.2252\n",
            "Epoch 63/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0224 - mape: 0.1260 - val_loss: 0.0814 - val_mape: 0.2247\n",
            "Epoch 64/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0220 - mape: 0.1243 - val_loss: 0.0810 - val_mape: 0.2241\n",
            "Epoch 65/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0216 - mape: 0.1227 - val_loss: 0.0807 - val_mape: 0.2236\n",
            "Epoch 66/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0212 - mape: 0.1211 - val_loss: 0.0805 - val_mape: 0.2231\n",
            "Epoch 67/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0209 - mape: 0.1197 - val_loss: 0.0802 - val_mape: 0.2227\n",
            "Epoch 68/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0205 - mape: 0.1183 - val_loss: 0.0799 - val_mape: 0.2223\n",
            "Epoch 69/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0202 - mape: 0.1170 - val_loss: 0.0797 - val_mape: 0.2220\n",
            "Epoch 70/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0199 - mape: 0.1158 - val_loss: 0.0795 - val_mape: 0.2217\n",
            "Epoch 71/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0197 - mape: 0.1147 - val_loss: 0.0793 - val_mape: 0.2215\n",
            "Epoch 72/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0194 - mape: 0.1136 - val_loss: 0.0792 - val_mape: 0.2214\n",
            "Epoch 73/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0192 - mape: 0.1126 - val_loss: 0.0791 - val_mape: 0.2214\n",
            "Epoch 74/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0189 - mape: 0.1116 - val_loss: 0.0790 - val_mape: 0.2215\n",
            "Epoch 75/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0187 - mape: 0.1107 - val_loss: 0.0790 - val_mape: 0.2217\n",
            "Epoch 76/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0185 - mape: 0.1098 - val_loss: 0.0789 - val_mape: 0.2220\n",
            "Epoch 77/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0183 - mape: 0.1090 - val_loss: 0.0789 - val_mape: 0.2223\n",
            "Epoch 78/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0182 - mape: 0.1082 - val_loss: 0.0789 - val_mape: 0.2228\n",
            "Epoch 79/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0180 - mape: 0.1075 - val_loss: 0.0790 - val_mape: 0.2233\n",
            "Epoch 80/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0178 - mape: 0.1069 - val_loss: 0.0791 - val_mape: 0.2239\n",
            "Epoch 81/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0177 - mape: 0.1063 - val_loss: 0.0792 - val_mape: 0.2245\n",
            "Epoch 82/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0176 - mape: 0.1057 - val_loss: 0.0793 - val_mape: 0.2252\n",
            "Epoch 83/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0174 - mape: 0.1051 - val_loss: 0.0794 - val_mape: 0.2259\n",
            "Epoch 84/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0173 - mape: 0.1046 - val_loss: 0.0795 - val_mape: 0.2267\n",
            "Epoch 85/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0172 - mape: 0.1041 - val_loss: 0.0796 - val_mape: 0.2275\n",
            "Epoch 86/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0171 - mape: 0.1036 - val_loss: 0.0798 - val_mape: 0.2282\n",
            "Epoch 87/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0170 - mape: 0.1032 - val_loss: 0.0799 - val_mape: 0.2290\n",
            "Epoch 88/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0169 - mape: 0.1028 - val_loss: 0.0801 - val_mape: 0.2297\n",
            "Epoch 89/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0168 - mape: 0.1024 - val_loss: 0.0802 - val_mape: 0.2304\n",
            "Epoch 90/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0167 - mape: 0.1020 - val_loss: 0.0803 - val_mape: 0.2311\n",
            "Epoch 91/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0166 - mape: 0.1017 - val_loss: 0.0805 - val_mape: 0.2318\n",
            "Epoch 92/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0165 - mape: 0.1013 - val_loss: 0.0806 - val_mape: 0.2324\n",
            "Epoch 93/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0165 - mape: 0.1010 - val_loss: 0.0807 - val_mape: 0.2329\n",
            "Epoch 94/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0164 - mape: 0.1007 - val_loss: 0.0808 - val_mape: 0.2335\n",
            "Epoch 95/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0163 - mape: 0.1004 - val_loss: 0.0809 - val_mape: 0.2339\n",
            "Epoch 96/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0162 - mape: 0.1001 - val_loss: 0.0810 - val_mape: 0.2344\n",
            "Epoch 97/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0162 - mape: 0.0998 - val_loss: 0.0811 - val_mape: 0.2348\n",
            "Epoch 98/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0161 - mape: 0.0995 - val_loss: 0.0811 - val_mape: 0.2351\n",
            "Epoch 99/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0160 - mape: 0.0992 - val_loss: 0.0812 - val_mape: 0.2355\n",
            "Epoch 100/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0160 - mape: 0.0990 - val_loss: 0.0812 - val_mape: 0.2357\n",
            "Epoch 101/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0159 - mape: 0.0987 - val_loss: 0.0812 - val_mape: 0.2358\n",
            "Epoch 102/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0158 - mape: 0.0985 - val_loss: 0.0812 - val_mape: 0.2359\n",
            "Epoch 103/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0158 - mape: 0.0982 - val_loss: 0.0812 - val_mape: 0.2359\n",
            "Epoch 104/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0157 - mape: 0.0980 - val_loss: 0.0811 - val_mape: 0.2357\n",
            "Epoch 105/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0156 - mape: 0.0977 - val_loss: 0.0810 - val_mape: 0.2355\n",
            "Epoch 106/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0156 - mape: 0.0975 - val_loss: 0.0809 - val_mape: 0.2352\n",
            "Epoch 107/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0155 - mape: 0.0972 - val_loss: 0.0807 - val_mape: 0.2347\n",
            "Epoch 108/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0154 - mape: 0.0970 - val_loss: 0.0805 - val_mape: 0.2342\n",
            "Epoch 109/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0154 - mape: 0.0967 - val_loss: 0.0803 - val_mape: 0.2335\n",
            "Epoch 110/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0153 - mape: 0.0964 - val_loss: 0.0800 - val_mape: 0.2326\n",
            "Epoch 111/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0152 - mape: 0.0961 - val_loss: 0.0797 - val_mape: 0.2316\n",
            "Epoch 112/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0151 - mape: 0.0958 - val_loss: 0.0794 - val_mape: 0.2305\n",
            "Epoch 113/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0151 - mape: 0.0955 - val_loss: 0.0790 - val_mape: 0.2292\n",
            "Epoch 114/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0150 - mape: 0.0952 - val_loss: 0.0786 - val_mape: 0.2278\n",
            "Epoch 115/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0149 - mape: 0.0949 - val_loss: 0.0781 - val_mape: 0.2261\n",
            "Epoch 116/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0148 - mape: 0.0945 - val_loss: 0.0777 - val_mape: 0.2244\n",
            "Epoch 117/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0147 - mape: 0.0942 - val_loss: 0.0772 - val_mape: 0.2224\n",
            "Epoch 118/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0146 - mape: 0.0938 - val_loss: 0.0767 - val_mape: 0.2202\n",
            "Epoch 119/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0145 - mape: 0.0934 - val_loss: 0.0761 - val_mape: 0.2180\n",
            "Epoch 120/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0144 - mape: 0.0930 - val_loss: 0.0756 - val_mape: 0.2156\n",
            "Epoch 121/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0143 - mape: 0.0926 - val_loss: 0.0750 - val_mape: 0.2132\n",
            "Epoch 122/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0142 - mape: 0.0921 - val_loss: 0.0745 - val_mape: 0.2107\n",
            "Epoch 123/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0141 - mape: 0.0916 - val_loss: 0.0741 - val_mape: 0.2081\n",
            "Epoch 124/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0140 - mape: 0.0912 - val_loss: 0.0736 - val_mape: 0.2055\n",
            "Epoch 125/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0138 - mape: 0.0907 - val_loss: 0.0733 - val_mape: 0.2032\n",
            "Epoch 126/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0137 - mape: 0.0903 - val_loss: 0.0732 - val_mape: 0.2016\n",
            "Epoch 127/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0136 - mape: 0.0898 - val_loss: 0.0731 - val_mape: 0.2009\n",
            "Epoch 128/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0135 - mape: 0.0894 - val_loss: 0.0733 - val_mape: 0.2019\n",
            "Epoch 129/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0134 - mape: 0.0890 - val_loss: 0.0737 - val_mape: 0.2050\n",
            "Epoch 130/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0133 - mape: 0.0886 - val_loss: 0.0744 - val_mape: 0.2092\n",
            "Epoch 131/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0132 - mape: 0.0882 - val_loss: 0.0754 - val_mape: 0.2143\n",
            "Epoch 132/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0131 - mape: 0.0879 - val_loss: 0.0766 - val_mape: 0.2200\n",
            "Epoch 133/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0130 - mape: 0.0876 - val_loss: 0.0780 - val_mape: 0.2263\n",
            "Epoch 134/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0129 - mape: 0.0873 - val_loss: 0.0795 - val_mape: 0.2330\n",
            "Epoch 135/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0128 - mape: 0.0871 - val_loss: 0.0812 - val_mape: 0.2398\n",
            "Epoch 136/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0128 - mape: 0.0870 - val_loss: 0.0828 - val_mape: 0.2461\n",
            "Epoch 137/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0127 - mape: 0.0868 - val_loss: 0.0843 - val_mape: 0.2516\n",
            "Epoch 138/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0126 - mape: 0.0867 - val_loss: 0.0855 - val_mape: 0.2562\n",
            "Epoch 139/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0126 - mape: 0.0865 - val_loss: 0.0866 - val_mape: 0.2596\n",
            "Epoch 140/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0126 - mape: 0.0864 - val_loss: 0.0872 - val_mape: 0.2619\n",
            "Epoch 141/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0125 - mape: 0.0862 - val_loss: 0.0876 - val_mape: 0.2630\n",
            "Epoch 142/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0125 - mape: 0.0861 - val_loss: 0.0876 - val_mape: 0.2631\n",
            "Epoch 143/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0124 - mape: 0.0859 - val_loss: 0.0873 - val_mape: 0.2621\n",
            "Epoch 144/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0124 - mape: 0.0858 - val_loss: 0.0868 - val_mape: 0.2604\n",
            "Epoch 145/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0124 - mape: 0.0857 - val_loss: 0.0860 - val_mape: 0.2579\n",
            "Epoch 146/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0124 - mape: 0.0855 - val_loss: 0.0852 - val_mape: 0.2550\n",
            "Epoch 147/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0124 - mape: 0.0854 - val_loss: 0.0842 - val_mape: 0.2517\n",
            "Epoch 148/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0123 - mape: 0.0853 - val_loss: 0.0833 - val_mape: 0.2482\n",
            "Epoch 149/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0123 - mape: 0.0852 - val_loss: 0.0823 - val_mape: 0.2447\n",
            "Epoch 150/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0123 - mape: 0.0851 - val_loss: 0.0814 - val_mape: 0.2412\n",
            "Epoch 151/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0123 - mape: 0.0850 - val_loss: 0.0806 - val_mape: 0.2379\n",
            "Epoch 152/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0123 - mape: 0.0849 - val_loss: 0.0798 - val_mape: 0.2348\n",
            "Epoch 153/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0123 - mape: 0.0848 - val_loss: 0.0791 - val_mape: 0.2320\n",
            "Epoch 154/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0123 - mape: 0.0847 - val_loss: 0.0785 - val_mape: 0.2294\n",
            "Epoch 155/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0123 - mape: 0.0846 - val_loss: 0.0780 - val_mape: 0.2272\n",
            "Epoch 156/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0123 - mape: 0.0845 - val_loss: 0.0776 - val_mape: 0.2252\n",
            "Epoch 157/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0122 - mape: 0.0844 - val_loss: 0.0772 - val_mape: 0.2236\n",
            "Epoch 158/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0122 - mape: 0.0844 - val_loss: 0.0768 - val_mape: 0.2221\n",
            "Epoch 159/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0122 - mape: 0.0843 - val_loss: 0.0765 - val_mape: 0.2208\n",
            "Epoch 160/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0122 - mape: 0.0842 - val_loss: 0.0762 - val_mape: 0.2196\n",
            "Epoch 161/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0122 - mape: 0.0842 - val_loss: 0.0760 - val_mape: 0.2186\n",
            "Epoch 162/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0122 - mape: 0.0841 - val_loss: 0.0757 - val_mape: 0.2176\n",
            "Epoch 163/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0122 - mape: 0.0841 - val_loss: 0.0755 - val_mape: 0.2167\n",
            "Epoch 164/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0122 - mape: 0.0840 - val_loss: 0.0753 - val_mape: 0.2159\n",
            "Epoch 165/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0122 - mape: 0.0840 - val_loss: 0.0752 - val_mape: 0.2151\n",
            "Epoch 166/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0122 - mape: 0.0839 - val_loss: 0.0750 - val_mape: 0.2144\n",
            "Epoch 167/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0122 - mape: 0.0839 - val_loss: 0.0748 - val_mape: 0.2137\n",
            "Epoch 168/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0838 - val_loss: 0.0747 - val_mape: 0.2131\n",
            "Epoch 169/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0838 - val_loss: 0.0746 - val_mape: 0.2125\n",
            "Epoch 170/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0837 - val_loss: 0.0744 - val_mape: 0.2118\n",
            "Epoch 171/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0837 - val_loss: 0.0743 - val_mape: 0.2112\n",
            "Epoch 172/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0837 - val_loss: 0.0742 - val_mape: 0.2107\n",
            "Epoch 173/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0121 - mape: 0.0836 - val_loss: 0.0740 - val_mape: 0.2101\n",
            "Epoch 174/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0121 - mape: 0.0836 - val_loss: 0.0739 - val_mape: 0.2096\n",
            "Epoch 175/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0836 - val_loss: 0.0738 - val_mape: 0.2091\n",
            "Epoch 176/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0835 - val_loss: 0.0737 - val_mape: 0.2086\n",
            "Epoch 177/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0835 - val_loss: 0.0736 - val_mape: 0.2082\n",
            "Epoch 178/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0835 - val_loss: 0.0735 - val_mape: 0.2078\n",
            "Epoch 179/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0835 - val_loss: 0.0734 - val_mape: 0.2073\n",
            "Epoch 180/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0834 - val_loss: 0.0733 - val_mape: 0.2068\n",
            "Epoch 181/200\n",
            "481/481 [==============================] - 3s 6ms/step - loss: 0.0121 - mape: 0.0834 - val_loss: 0.0732 - val_mape: 0.2063\n",
            "Epoch 182/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0834 - val_loss: 0.0731 - val_mape: 0.2059\n",
            "Epoch 183/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0833 - val_loss: 0.0730 - val_mape: 0.2054\n",
            "Epoch 184/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0833 - val_loss: 0.0729 - val_mape: 0.2049\n",
            "Epoch 185/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0121 - mape: 0.0833 - val_loss: 0.0728 - val_mape: 0.2044\n",
            "Epoch 186/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0121 - mape: 0.0833 - val_loss: 0.0727 - val_mape: 0.2038\n",
            "Epoch 187/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0832 - val_loss: 0.0726 - val_mape: 0.2033\n",
            "Epoch 188/200\n",
            "481/481 [==============================] - 3s 5ms/step - loss: 0.0120 - mape: 0.0832 - val_loss: 0.0725 - val_mape: 0.2028\n",
            "Epoch 189/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0832 - val_loss: 0.0724 - val_mape: 0.2023\n",
            "Epoch 190/200\n",
            "481/481 [==============================] - 2s 3ms/step - loss: 0.0120 - mape: 0.0832 - val_loss: 0.0723 - val_mape: 0.2018\n",
            "Epoch 191/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0831 - val_loss: 0.0722 - val_mape: 0.2013\n",
            "Epoch 192/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0831 - val_loss: 0.0721 - val_mape: 0.2009\n",
            "Epoch 193/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0831 - val_loss: 0.0720 - val_mape: 0.2004\n",
            "Epoch 194/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0120 - mape: 0.0831 - val_loss: 0.0720 - val_mape: 0.2000\n",
            "Epoch 195/200\n",
            "481/481 [==============================] - 2s 5ms/step - loss: 0.0120 - mape: 0.0830 - val_loss: 0.0719 - val_mape: 0.1996\n",
            "Epoch 196/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0830 - val_loss: 0.0718 - val_mape: 0.1991\n",
            "Epoch 197/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0830 - val_loss: 0.0717 - val_mape: 0.1987\n",
            "Epoch 198/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0830 - val_loss: 0.0717 - val_mape: 0.1984\n",
            "Epoch 199/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0829 - val_loss: 0.0716 - val_mape: 0.1980\n",
            "Epoch 200/200\n",
            "481/481 [==============================] - 2s 4ms/step - loss: 0.0120 - mape: 0.0829 - val_loss: 0.0715 - val_mape: 0.1977\n"
          ]
        },
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          "data": {
            "text/plain": [
              "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>epoch/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/loss</td><td>█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/mape</td><td>█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/val_loss</td><td>█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/val_mape</td><td>█▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch/epoch</td><td>199</td></tr><tr><td>epoch/learning_rate</td><td>0.001</td></tr><tr><td>epoch/loss</td><td>0.012</td></tr><tr><td>epoch/mape</td><td>0.0829</td></tr><tr><td>epoch/val_loss</td><td>0.07154</td></tr><tr><td>epoch/val_mape</td><td>0.19769</td></tr></table><br/></div></div>"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">model LSTM on stock data MSN.csv</strong> at: <a href='https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi' target=\"_blank\">https://wandb.ai/nguyentienthong2125/Finpros/runs/a1cbiuwi</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20240307_045428-a1cbiuwi/logs</code>"
            ]
          },
          "metadata": {}
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#V. Prediction\n"
      ],
      "metadata": {
        "id": "2ElthCj-yAlJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred= lstm.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Obn6r__ryCwd",
        "outputId": "14d41599-c404-4942-82ca-4d9f46549992"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "385/385 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.plot(y_test, label='True Value')\n",
        "plt.plot(y_pred, label='LSTM Value')\n",
        "plt.title('Prediction by LSTM')\n",
        "plt.xlabel('Time Scale')\n",
        "plt.ylabel('Scaled vnd')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "k90wG52izlk2",
        "outputId": "866db2d7-fa06-4a30-a77b-3e9c78158420"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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}