Forcast-Forex-With-Deep-Learning-Models.ipynb

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        "<a href=\"https://colab.research.google.com/github/MahdiNavaei/Forcast-Forex-With-Deep-Learning-Models/blob/main/Forcast-Forex-With-Deep-Learning-Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Import necessary libraries\n"
      ],
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        "id": "3Sg-tq1ce6vZ"
      }
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    {
      "cell_type": "code",
      "execution_count": 2,
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        "id": "0A_9_3joVu7D"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import requests\n",
        "from datetime import datetime,timedelta\n",
        "import scipy\n",
        "import pylab\n",
        "import statsmodels.tsa.stattools as sts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set API key\n",
        "api_key = 'e61479fbc59d62f40c959bd6021e9df48b79c324'"
      ],
      "metadata": {
        "id": "LVphvkBJXMB9"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set start and end dates for data retrieval\n",
        "\n",
        "end = datetime.now()\n",
        "start = end - timedelta(days=365*2)\n",
        "start_date = start.replace(hour=0, minute=0, second=0, microsecond=0)\n",
        "end_date = end.replace(hour=23, minute=59, second=59, microsecond=999999)\n"
      ],
      "metadata": {
        "id": "1OHy6L4Kbqj1"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the ticker and interval for the Tiingo API\n",
        "\n",
        "ticker = 'EURJPY'\n",
        "interval = '1hour'"
      ],
      "metadata": {
        "id": "9AYX36Zkbs3j"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the URL to retrieve the data\n",
        "\n",
        "url = f'https://api.tiingo.com/tiingo/fx/{ticker}/prices?startDate={start_date}&endDate={end_date}&resampleFreq={interval}&token={api_key}'\n"
      ],
      "metadata": {
        "id": "fj8xsgYebwcw"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve data from the API\n",
        "\n",
        "response = requests.get(url)"
      ],
      "metadata": {
        "id": "XH9a3eI4byW3"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if the API call was successful and store the data in a pandas DataFrame\n",
        "\n",
        "if response.status_code == 200:\n",
        "    data = response.json()\n",
        "    df = pd.json_normalize(data)\n",
        "\n",
        "    # ذخیره داده ها در یک فایل اکسل\n",
        "    df.to_excel('stock_data.xlsx', index=False)\n",
        "    print('Data saved successfully.')\n",
        "else:\n",
        "    print(\"Error receiving data\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uaxHPznb5fA",
        "outputId": "bf4fdef2-ce9e-4433-9767-b8d0d7a4b8f0"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data saved successfully.\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "\n",
        "df"
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              "                          date  ticker      open      high       low     close\n",
              "0     2021-03-28T21:00:00.000Z  eurjpy  129.3800  129.4125  129.3465  129.3675\n",
              "1     2021-03-28T22:00:00.000Z  eurjpy  129.3660  129.4235  129.3045  129.4225\n",
              "2     2021-03-28T23:00:00.000Z  eurjpy  129.4220  129.4335  129.3780  129.4195\n",
              "3     2021-03-29T00:00:00.000Z  eurjpy  129.4190  129.4255  129.1465  129.1975\n",
              "4     2021-03-29T01:00:00.000Z  eurjpy  129.1970  129.2635  129.1585  129.2005\n",
              "...                        ...     ...       ...       ...       ...       ...\n",
              "6994  2022-05-17T12:00:00.000Z  eurjpy  136.3030  136.6340  136.2265  136.6170\n",
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        "df= pd.DataFrame(df)\n",
        "# Convert the 'date' column to a datetime format\n",
        "\n",
        "df['date']=pd.to_datetime(df['date'])\n",
        "df"
      ],
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              "                          date  ticker      open      high       low     close\n",
              "0    2021-03-28 21:00:00+00:00  eurjpy  129.3800  129.4125  129.3465  129.3675\n",
              "1    2021-03-28 22:00:00+00:00  eurjpy  129.3660  129.4235  129.3045  129.4225\n",
              "2    2021-03-28 23:00:00+00:00  eurjpy  129.4220  129.4335  129.3780  129.4195\n",
              "3    2021-03-29 00:00:00+00:00  eurjpy  129.4190  129.4255  129.1465  129.1975\n",
              "4    2021-03-29 01:00:00+00:00  eurjpy  129.1970  129.2635  129.1585  129.2005\n",
              "...                        ...     ...       ...       ...       ...       ...\n",
              "6994 2022-05-17 12:00:00+00:00  eurjpy  136.3030  136.6340  136.2265  136.6170\n",
              "6995 2022-05-17 13:00:00+00:00  eurjpy  136.6175  136.6985  136.3755  136.5930\n",
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              "6997 2022-05-17 15:00:00+00:00  eurjpy  136.2065  136.3580  136.1520  136.2090\n",
              "6998 2022-05-17 16:00:00+00:00  eurjpy  136.2085  136.2110  136.1930  136.2100\n",
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              "      <td>2021-03-28 22:00:00+00:00</td>\n",
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              "      <td>136.6340</td>\n",
              "      <td>136.2265</td>\n",
              "      <td>136.6170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6995</th>\n",
              "      <td>2022-05-17 13:00:00+00:00</td>\n",
              "      <td>eurjpy</td>\n",
              "      <td>136.6175</td>\n",
              "      <td>136.6985</td>\n",
              "      <td>136.3755</td>\n",
              "      <td>136.5930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6996</th>\n",
              "      <td>2022-05-17 14:00:00+00:00</td>\n",
              "      <td>eurjpy</td>\n",
              "      <td>136.5940</td>\n",
              "      <td>136.6290</td>\n",
              "      <td>135.9805</td>\n",
              "      <td>136.2070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6997</th>\n",
              "      <td>2022-05-17 15:00:00+00:00</td>\n",
              "      <td>eurjpy</td>\n",
              "      <td>136.2065</td>\n",
              "      <td>136.3580</td>\n",
              "      <td>136.1520</td>\n",
              "      <td>136.2090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6998</th>\n",
              "      <td>2022-05-17 16:00:00+00:00</td>\n",
              "      <td>eurjpy</td>\n",
              "      <td>136.2085</td>\n",
              "      <td>136.2110</td>\n",
              "      <td>136.1930</td>\n",
              "      <td>136.2100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6999 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c955d15a-98fb-4dc6-95aa-42c85234d4da')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c955d15a-98fb-4dc6-95aa-42c85234d4da 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-c955d15a-98fb-4dc6-95aa-42c85234d4da');\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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the 'ticker' column from the DataFrame\n",
        "\n",
        "df=df.drop('ticker',axis=1)"
      ],
      "metadata": {
        "id": "aKf3qdIrjDrH"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'] = df['date'].dt.strftime('%d-%m-%Y %H:%M:%S')\n"
      ],
      "metadata": {
        "id": "gNLouLSZcE2d"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the index to the 'date' column\n",
        "\n",
        "df.set_index('date',inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "oX3ameJEcJJd",
        "outputId": "bd4a8c1e-1438-4013-a75e-65c77ceadce7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         open      high       low     close\n",
              "date                                                       \n",
              "28-03-2021 21:00:00  129.3800  129.4125  129.3465  129.3675\n",
              "28-03-2021 22:00:00  129.3660  129.4235  129.3045  129.4225\n",
              "28-03-2021 23:00:00  129.4220  129.4335  129.3780  129.4195\n",
              "29-03-2021 00:00:00  129.4190  129.4255  129.1465  129.1975\n",
              "29-03-2021 01:00:00  129.1970  129.2635  129.1585  129.2005\n",
              "...                       ...       ...       ...       ...\n",
              "17-05-2022 12:00:00  136.3030  136.6340  136.2265  136.6170\n",
              "17-05-2022 13:00:00  136.6175  136.6985  136.3755  136.5930\n",
              "17-05-2022 14:00:00  136.5940  136.6290  135.9805  136.2070\n",
              "17-05-2022 15:00:00  136.2065  136.3580  136.1520  136.2090\n",
              "17-05-2022 16:00:00  136.2085  136.2110  136.1930  136.2100\n",
              "\n",
              "[6999 rows x 4 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "<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>close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>28-03-2021 21:00:00</th>\n",
              "      <td>129.3800</td>\n",
              "      <td>129.4125</td>\n",
              "      <td>129.3465</td>\n",
              "      <td>129.3675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28-03-2021 22:00:00</th>\n",
              "      <td>129.3660</td>\n",
              "      <td>129.4235</td>\n",
              "      <td>129.3045</td>\n",
              "      <td>129.4225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28-03-2021 23:00:00</th>\n",
              "      <td>129.4220</td>\n",
              "      <td>129.4335</td>\n",
              "      <td>129.3780</td>\n",
              "      <td>129.4195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29-03-2021 00:00:00</th>\n",
              "      <td>129.4190</td>\n",
              "      <td>129.4255</td>\n",
              "      <td>129.1465</td>\n",
              "      <td>129.1975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29-03-2021 01:00:00</th>\n",
              "      <td>129.1970</td>\n",
              "      <td>129.2635</td>\n",
              "      <td>129.1585</td>\n",
              "      <td>129.2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-05-2022 12:00:00</th>\n",
              "      <td>136.3030</td>\n",
              "      <td>136.6340</td>\n",
              "      <td>136.2265</td>\n",
              "      <td>136.6170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-05-2022 13:00:00</th>\n",
              "      <td>136.6175</td>\n",
              "      <td>136.6985</td>\n",
              "      <td>136.3755</td>\n",
              "      <td>136.5930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-05-2022 14:00:00</th>\n",
              "      <td>136.5940</td>\n",
              "      <td>136.6290</td>\n",
              "      <td>135.9805</td>\n",
              "      <td>136.2070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-05-2022 15:00:00</th>\n",
              "      <td>136.2065</td>\n",
              "      <td>136.3580</td>\n",
              "      <td>136.1520</td>\n",
              "      <td>136.2090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-05-2022 16:00:00</th>\n",
              "      <td>136.2085</td>\n",
              "      <td>136.2110</td>\n",
              "      <td>136.1930</td>\n",
              "      <td>136.2100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6999 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50df7e7a-1e43-429d-a88d-576ef22b7c25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <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",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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-50df7e7a-1e43-429d-a88d-576ef22b7c25 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-50df7e7a-1e43-429d-a88d-576ef22b7c25');\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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the high values of the DataFrame using a probability plot\n",
        "\n",
        "scipy.stats.probplot(df.high,plot=pylab)\n",
        "pylab.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "e2YnGk_NcLel",
        "outputId": "2ffbba6c-f6af-4a53-8ea8-2919689fd35d"
      },
      "execution_count": 56,
      "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": [
        "# Run the augmented Dickey-Fuller test on the high values of the DataFrame\n",
        "\n",
        "sts.adfuller(df.high)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_C6zkcjcoRs",
        "outputId": "356ffb72-7333-47e6-c049-686eb916bbae"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.6780492580379704,\n",
              " 0.44245616209458877,\n",
              " 3,\n",
              " 6995,\n",
              " {'1%': -3.431285196760183,\n",
              "  '5%': -2.8619532817881064,\n",
              "  '10%': -2.566989985928885},\n",
              " -10753.30029666773)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the DataFrame\n",
        "\n",
        "df.plot(figsize=(15,15),subplots=True)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "KU4QcbZTdN6z",
        "outputId": "dadf7420-556b-4803-90d9-66654d2ec2e9"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a histogram of the DataFrame\n",
        "\n",
        "df.hist(figsize=(25,10))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "jmVZBNQYeKPP",
        "outputId": "ed4fb24c-5356-4e64-f3ea-0a6bc7626e79"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the autocorrelation of the high values of the DataFrame\n",
        "\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "plot_acf(df.high,lags=50,zero=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "CBC3s1_9ePOf",
        "outputId": "0e4641ce-e2c4-4038-cc34-90a553dc1908"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "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": [
        "# Set the lag and training split\n",
        "\n",
        "lag=3\n",
        "train_split = int(len(df)*0.8)\n",
        "train_split"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prqLUoKMeTLj",
        "outputId": "47af3f56-9195-4e65-a046-cbde7323f8a2"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5599"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the DataFrame to a numpy array\n",
        "\n",
        "df=np.array(df)"
      ],
      "metadata": {
        "id": "GRq009LoeuA2"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to create multivariate data\n",
        "\n",
        "def multivariate_data(dataset, target, start_index, end_index, history_size,\n",
        "                      target_size, step, single_step=False):\n",
        "    data = []\n",
        "    labels = []\n",
        "\n",
        "    start_index = start_index + history_size\n",
        "    if end_index is None:\n",
        "        end_index = len(dataset) - target_size\n",
        "\n",
        "    for i in range(start_index, end_index):\n",
        "        indices = range(i-history_size, i, step)\n",
        "        data.append(dataset[indices])\n",
        "\n",
        "        if single_step:\n",
        "            labels.append(target[i+target_size])\n",
        "        else:\n",
        "            labels.append(target[i:i+target_size])\n",
        "\n",
        "    return np.array(data), np.array(labels)"
      ],
      "metadata": {
        "id": "Pns5saIaexwu"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the number of steps, past history, and step size for the multivariate data\n",
        "\n",
        "n_step = 1\n",
        "past_history = lag\n",
        "n_step= 1\n",
        "STEP = 1"
      ],
      "metadata": {
        "id": "BYp8Sfs4e-aX"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split dataset into training and testing sets\n",
        "\n",
        "x_train_single, y_train_single = multivariate_data(df, df[:, 0], 0,\n",
        "                                                   train_split, past_history,\n",
        "                                                   n_step, STEP,\n",
        "                                                   single_step=True)\n",
        "x_test_single, y_test_single = multivariate_data(df, df[:, 0],\n",
        "                                               train_split, None, past_history,\n",
        "                                               n_step, STEP,\n",
        "                                               single_step=True)"
      ],
      "metadata": {
        "id": "ePBYrnR7fD8H"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the shapes of the training and testing sets\n",
        "\n",
        "print(x_train_single.shape)\n",
        "print(x_test_single.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKq6EtQqfF1D",
        "outputId": "1d2d8e1b-0a98-499c-9f94-881dcd9c6b1c"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5596, 3, 4)\n",
            "(1396, 3, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train_single.shape)\n",
        "print(y_test_single.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lodca8VfHYw",
        "outputId": "b541a946-249e-4e37-f93d-ff5ebe6b2886"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5596,)\n",
            "(1396,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first sample of the training set\n",
        "\n",
        "x_train_single[0,:,0:7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJOOLdEhfS1g",
        "outputId": "c998a196-9b44-439e-d272-b7f794d8ed05"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[129.38  , 129.4125, 129.3465, 129.3675],\n",
              "       [129.366 , 129.4235, 129.3045, 129.4225],\n",
              "       [129.422 , 129.4335, 129.378 , 129.4195]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#scaling data"
      ],
      "metadata": {
        "id": "80sy3h3pfXEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "mfcficEqfUgk"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary to store scalers for each feature\n",
        "\n",
        "scalers = {}\n",
        "for i in range(x_train_single.shape[1]):\n",
        "    scalers [i]= MinMaxScaler(feature_range=(0,1))\n",
        "    x_train_single[:, i, :] = scalers[i].fit_transform(x_train_single[:, i, :]) \n",
        "\n",
        "for i in range(x_test_single.shape[1]):\n",
        "    x_test_single[:, i, :] = scalers[i].transform(x_test_single[:, i, :]) "
      ],
      "metadata": {
        "id": "vLQ3VIzOfZmo"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first sample of the training set after scaling\n",
        "\n",
        "x_train_single[0,:,0:7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtH4jCFDfbYM",
        "outputId": "eee0d1c1-5899-44b1-9fc3-8946f4ed6e1e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.2971215 , 0.28353218, 0.29608939, 0.29526088],\n",
              "       [0.29502804, 0.28520404, 0.28974785, 0.30348333],\n",
              "       [0.30340187, 0.28672392, 0.30084554, 0.30303483]])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the shape of the last two dimensions of the training set\n",
        "\n",
        "x_train_single.shape[-2:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCEixec7F9kM",
        "outputId": "c471311c-7c08-4d4e-e936-695f7f5959e6"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the target variables using MinMaxScaler\n",
        "\n",
        "\n",
        "y_train_single=y_train_single.reshape(-1,1)\n",
        "\n",
        "y_test_single=y_test_single.reshape(-1,1)\n",
        "\n",
        "\n",
        "scaler =MinMaxScaler(feature_range=(0,1))\n",
        "y_train_single=scaler.fit_transform(y_train_single)\n",
        "y_test_single=scaler.transform(y_test_single)"
      ],
      "metadata": {
        "id": "NydIUpxWGBof"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeling"
      ],
      "metadata": {
        "id": "j4sjelC8GOaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "\n",
        "from keras.layers import LSTM,Dense,GRU,SimpleRNN,Flatten,Dropout\n",
        "\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "gdqreCWxGRmu"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RNN"
      ],
      "metadata": {
        "id": "vkgobEfdGKuc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define RNN model architecture\n",
        "\n",
        "RNN_model = Sequential()\n",
        "RNN_model.add(SimpleRNN(50,input_shape=x_train_single.shape[-2:],activation='relu',return_sequences=True,kernel_regularizer='l2'))\n",
        "RNN_model.add(Dropout(0.3))\n",
        "\n",
        "RNN_model.add(SimpleRNN(50,activation='relu',kernel_regularizer='l2'))\n",
        "RNN_model.add(Dropout(0.3))\n",
        "\n",
        "RNN_model.add(Dense(n_step,activation='selu'))\n",
        "\n",
        "RNN_model.compile(optimizer='adam', loss='mse',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "PKNs7ZKHGIQf"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logdir='logs'\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\n",
        "\n",
        "history=RNN_model.fit(x_train_single,y_train_single,epochs=100,batch_size=16,\n",
        "              validation_data=(x_test_single,y_test_single),callbacks=[tensorboard_callback])\n",
        "print(history.history) # this will print a dictionary object, now you need to grab the metrics / score you're looking for\n",
        "\n",
        "# if your score == 'acc', if not replace 'acc' with your metric\n",
        "\n",
        "best_score = max(history.history['val_accuracy'])\n",
        "\n",
        "print(f\"Best Validation score is: {best_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjJz_mPOGY1-",
        "outputId": "7832ff4d-e5fd-4a5b-989e-427228981325"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "350/350 [==============================] - 6s 10ms/step - loss: 0.2478 - accuracy: 3.5740e-04 - val_loss: 0.1047 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0480 - accuracy: 3.5740e-04 - val_loss: 0.0524 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0193 - accuracy: 3.5740e-04 - val_loss: 0.0381 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0102 - accuracy: 3.5740e-04 - val_loss: 0.0388 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0068 - accuracy: 3.5740e-04 - val_loss: 0.0358 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0058 - accuracy: 3.5740e-04 - val_loss: 0.0457 - val_accuracy: 0.0000e+00\n",
            "Epoch 7/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0050 - accuracy: 3.5740e-04 - val_loss: 0.0473 - val_accuracy: 0.0000e+00\n",
            "Epoch 8/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0047 - accuracy: 3.5740e-04 - val_loss: 0.0251 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/100\n",
            "350/350 [==============================] - 8s 23ms/step - loss: 0.0043 - accuracy: 3.5740e-04 - val_loss: 0.0295 - val_accuracy: 0.0000e+00\n",
            "Epoch 10/100\n",
            "350/350 [==============================] - 4s 10ms/step - loss: 0.0040 - accuracy: 3.5740e-04 - val_loss: 0.0466 - val_accuracy: 0.0000e+00\n",
            "Epoch 11/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0042 - accuracy: 3.5740e-04 - val_loss: 0.0393 - val_accuracy: 0.0000e+00\n",
            "Epoch 12/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0043 - accuracy: 3.5740e-04 - val_loss: 0.0604 - val_accuracy: 0.0000e+00\n",
            "Epoch 13/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0042 - accuracy: 3.5740e-04 - val_loss: 0.0406 - val_accuracy: 0.0000e+00\n",
            "Epoch 14/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0039 - accuracy: 3.5740e-04 - val_loss: 0.0886 - val_accuracy: 0.0000e+00\n",
            "Epoch 15/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0040 - accuracy: 3.5740e-04 - val_loss: 0.0585 - val_accuracy: 0.0000e+00\n",
            "Epoch 16/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0040 - accuracy: 3.5740e-04 - val_loss: 0.0368 - val_accuracy: 0.0000e+00\n",
            "Epoch 17/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0572 - val_accuracy: 0.0000e+00\n",
            "Epoch 18/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0041 - accuracy: 3.5740e-04 - val_loss: 0.0389 - val_accuracy: 0.0000e+00\n",
            "Epoch 19/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0569 - val_accuracy: 0.0000e+00\n",
            "Epoch 20/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0039 - accuracy: 3.5740e-04 - val_loss: 0.0587 - val_accuracy: 0.0000e+00\n",
            "Epoch 21/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0312 - val_accuracy: 0.0000e+00\n",
            "Epoch 22/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0528 - val_accuracy: 0.0000e+00\n",
            "Epoch 23/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0634 - val_accuracy: 0.0000e+00\n",
            "Epoch 24/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0441 - val_accuracy: 0.0000e+00\n",
            "Epoch 25/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0651 - val_accuracy: 0.0000e+00\n",
            "Epoch 26/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0245 - val_accuracy: 0.0000e+00\n",
            "Epoch 27/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0041 - accuracy: 3.5740e-04 - val_loss: 0.0444 - val_accuracy: 0.0000e+00\n",
            "Epoch 28/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0511 - val_accuracy: 0.0000e+00\n",
            "Epoch 29/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0664 - val_accuracy: 0.0000e+00\n",
            "Epoch 30/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0551 - val_accuracy: 0.0000e+00\n",
            "Epoch 31/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0358 - val_accuracy: 0.0000e+00\n",
            "Epoch 32/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0467 - val_accuracy: 0.0000e+00\n",
            "Epoch 33/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0581 - val_accuracy: 0.0000e+00\n",
            "Epoch 34/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0659 - val_accuracy: 0.0000e+00\n",
            "Epoch 35/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0491 - val_accuracy: 0.0000e+00\n",
            "Epoch 36/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0039 - accuracy: 3.5740e-04 - val_loss: 0.0580 - val_accuracy: 0.0000e+00\n",
            "Epoch 37/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0291 - val_accuracy: 0.0000e+00\n",
            "Epoch 38/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0662 - val_accuracy: 0.0000e+00\n",
            "Epoch 39/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0441 - val_accuracy: 0.0000e+00\n",
            "Epoch 40/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0433 - val_accuracy: 0.0000e+00\n",
            "Epoch 41/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0357 - val_accuracy: 0.0000e+00\n",
            "Epoch 42/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0372 - val_accuracy: 0.0000e+00\n",
            "Epoch 43/100\n",
            "350/350 [==============================] - 5s 13ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0583 - val_accuracy: 0.0000e+00\n",
            "Epoch 44/100\n",
            "350/350 [==============================] - 5s 13ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.1016 - val_accuracy: 0.0000e+00\n",
            "Epoch 45/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0641 - val_accuracy: 0.0000e+00\n",
            "Epoch 46/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0038 - accuracy: 3.5740e-04 - val_loss: 0.0475 - val_accuracy: 0.0000e+00\n",
            "Epoch 47/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0773 - val_accuracy: 0.0000e+00\n",
            "Epoch 48/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0521 - val_accuracy: 0.0000e+00\n",
            "Epoch 49/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0596 - val_accuracy: 0.0000e+00\n",
            "Epoch 50/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0719 - val_accuracy: 0.0000e+00\n",
            "Epoch 51/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0425 - val_accuracy: 0.0000e+00\n",
            "Epoch 52/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0327 - val_accuracy: 0.0000e+00\n",
            "Epoch 53/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0513 - val_accuracy: 0.0000e+00\n",
            "Epoch 54/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0843 - val_accuracy: 0.0000e+00\n",
            "Epoch 55/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0465 - val_accuracy: 0.0000e+00\n",
            "Epoch 56/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0495 - val_accuracy: 0.0000e+00\n",
            "Epoch 57/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0562 - val_accuracy: 0.0000e+00\n",
            "Epoch 58/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0883 - val_accuracy: 0.0000e+00\n",
            "Epoch 59/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0902 - val_accuracy: 0.0000e+00\n",
            "Epoch 60/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0591 - val_accuracy: 0.0000e+00\n",
            "Epoch 61/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0641 - val_accuracy: 0.0000e+00\n",
            "Epoch 62/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0446 - val_accuracy: 0.0000e+00\n",
            "Epoch 63/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0536 - val_accuracy: 0.0000e+00\n",
            "Epoch 64/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0589 - val_accuracy: 0.0000e+00\n",
            "Epoch 65/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0730 - val_accuracy: 0.0000e+00\n",
            "Epoch 66/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0591 - val_accuracy: 0.0000e+00\n",
            "Epoch 67/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0518 - val_accuracy: 0.0000e+00\n",
            "Epoch 68/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0426 - val_accuracy: 0.0000e+00\n",
            "Epoch 69/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0592 - val_accuracy: 0.0000e+00\n",
            "Epoch 70/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0860 - val_accuracy: 0.0000e+00\n",
            "Epoch 71/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0032 - accuracy: 3.5740e-04 - val_loss: 0.0606 - val_accuracy: 0.0000e+00\n",
            "Epoch 72/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0433 - val_accuracy: 0.0000e+00\n",
            "Epoch 73/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.1193 - val_accuracy: 0.0000e+00\n",
            "Epoch 74/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0517 - val_accuracy: 0.0000e+00\n",
            "Epoch 75/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0529 - val_accuracy: 0.0000e+00\n",
            "Epoch 76/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0409 - val_accuracy: 0.0000e+00\n",
            "Epoch 77/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0601 - val_accuracy: 0.0000e+00\n",
            "Epoch 78/100\n",
            "350/350 [==============================] - 4s 11ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0307 - val_accuracy: 0.0000e+00\n",
            "Epoch 79/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0555 - val_accuracy: 0.0000e+00\n",
            "Epoch 80/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0522 - val_accuracy: 0.0000e+00\n",
            "Epoch 81/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0393 - val_accuracy: 0.0000e+00\n",
            "Epoch 82/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0614 - val_accuracy: 0.0000e+00\n",
            "Epoch 83/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0477 - val_accuracy: 0.0000e+00\n",
            "Epoch 84/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0844 - val_accuracy: 0.0000e+00\n",
            "Epoch 85/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0528 - val_accuracy: 0.0000e+00\n",
            "Epoch 86/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0381 - val_accuracy: 0.0000e+00\n",
            "Epoch 87/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0553 - val_accuracy: 0.0000e+00\n",
            "Epoch 88/100\n",
            "350/350 [==============================] - 4s 10ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0600 - val_accuracy: 0.0000e+00\n",
            "Epoch 89/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0036 - accuracy: 3.5740e-04 - val_loss: 0.0710 - val_accuracy: 0.0000e+00\n",
            "Epoch 90/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0649 - val_accuracy: 0.0000e+00\n",
            "Epoch 91/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0898 - val_accuracy: 0.0000e+00\n",
            "Epoch 92/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0233 - val_accuracy: 0.0000e+00\n",
            "Epoch 93/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0329 - val_accuracy: 0.0000e+00\n",
            "Epoch 94/100\n",
            "350/350 [==============================] - 4s 13ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0374 - val_accuracy: 0.0000e+00\n",
            "Epoch 95/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0034 - accuracy: 3.5740e-04 - val_loss: 0.0456 - val_accuracy: 0.0000e+00\n",
            "Epoch 96/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0037 - accuracy: 3.5740e-04 - val_loss: 0.0351 - val_accuracy: 0.0000e+00\n",
            "Epoch 97/100\n",
            "350/350 [==============================] - 4s 12ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0445 - val_accuracy: 0.0000e+00\n",
            "Epoch 98/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0408 - val_accuracy: 0.0000e+00\n",
            "Epoch 99/100\n",
            "350/350 [==============================] - 3s 9ms/step - loss: 0.0033 - accuracy: 3.5740e-04 - val_loss: 0.0375 - val_accuracy: 0.0000e+00\n",
            "Epoch 100/100\n",
            "350/350 [==============================] - 3s 10ms/step - loss: 0.0035 - accuracy: 3.5740e-04 - val_loss: 0.0429 - val_accuracy: 0.0000e+00\n",
            "{'loss': [0.24780696630477905, 0.04804559797048569, 0.01933913491666317, 0.010211944580078125, 0.006804903037846088, 0.005762386601418257, 0.004972095135599375, 0.004726823419332504, 0.004339788109064102, 0.0040133981965482235, 0.004187490325421095, 0.004298449959605932, 0.0041707782074809074, 0.003919088281691074, 0.003968916367739439, 0.00404971931129694, 0.0038405281957238913, 0.0041285064071416855, 0.003681407542899251, 0.003912126179784536, 0.003666473086923361, 0.003841744503006339, 0.0037956309970468283, 0.003750966629013419, 0.0037407809868454933, 0.003654424799606204, 0.004140788223594427, 0.0037182290107011795, 0.0035009863786399364, 0.0038232251536101103, 0.003756418591365218, 0.0036604979541152716, 0.0036799428053200245, 0.003756870748475194, 0.003643561154603958, 0.0038945963606238365, 0.0037564414087682962, 0.003553747432306409, 0.0037396238185465336, 0.0033979981672018766, 0.003578850068151951, 0.003589326748624444, 0.003773066448047757, 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            "Best Validation score is: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize model loss\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "plt.plot(history.history['loss'], c='r')\n",
        "\n",
        "\n",
        "plt.plot(history.history['val_loss'], c='b')\n",
        "\n",
        "plt.title('model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['train','validation'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "xPw4L7UwGbHX",
        "outputId": "0e184b69-adbf-4e93-a6fb-fb1695d013e0"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predict test set\n",
        "\n",
        "pred_rnn=RNN_model.predict(x_test_single)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuJ5h-c7IAnE",
        "outputId": "f8362994-2b9a-4c41-bb37-5b283d8586ae"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute MSE of prediction\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mse_rnn = mean_squared_error(y_test_single,pred_rnn)\n",
        "mse_rnn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-ys4GwcIHZw",
        "outputId": "6a4e7122-9241-48c9-9ff7-c0c0eeb83972"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.042132947044965945"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inverse Prediction\n",
        "\n",
        "predict_RNN_inv = scaler.inverse_transform(pred_rnn)\n",
        "\n",
        "y_test_inv = scaler.inverse_transform(y_test_single)"
      ],
      "metadata": {
        "id": "C_PXGE1DIJCQ"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize prediction results\n",
        "\n",
        "fig = plt.figure(figsize = (15,10))\n",
        "plt.plot(predict_RNN_inv, color='b', label='Real ')\n",
        "plt.plot(y_test_inv, color='r', label='Predicted ')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "P6kyoGS1IQbS",
        "outputId": "89c36ec4-e668-4c02-d74f-6b417b7362fa"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict one step\n",
        "input_samples=x_test_single[-1:,:,:]\n",
        "\n",
        "input_samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXFIbRNOIRq7",
        "outputId": "620ee667-52c7-4cb6-bcb2-5a258af1db66"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[1.33233645, 1.38110799, 1.33489355, 1.37905517],\n",
              "        [1.37936449, 1.39091116, 1.35739091, 1.37546718],\n",
              "        [1.37585047, 1.38034805, 1.29775026, 1.3177605 ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next_step_rnn = RNN_model.predict(input_samples)\n",
        "\n",
        "next_step_rnn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciBQpPg_Ibm4",
        "outputId": "ef0c3d2c-4c45-4c8d-8c33-e7dfcda1ae24"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.1500335]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LSTM"
      ],
      "metadata": {
        "id": "j3v9OTVaIhUr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LSTM_model = Sequential()\n",
        "\n",
        "LSTM_model.add(LSTM(50,input_shape=x_train_single.shape[-2:],activation='relu',return_sequences=True))\n",
        "LSTM_model.add(Dropout(0.3))\n",
        "\n",
        "LSTM_model.add(LSTM(50,activation='relu',kernel_regularizer='l2'))\n",
        "LSTM_model.add(Dropout(0.2))\n",
        "\n",
        "LSTM_model.add(Dense(n_step,activation='selu'))\n",
        "LSTM_model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7pyP53FIdEt",
        "outputId": "2f084517-5a32-4d5e-d88f-362359a2c3d8"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logdir='logs'\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\n",
        "\n",
        "history_LSTM=LSTM_model.fit(x_train_single,y_train_single,\n",
        "                                            epochs=100,batch_size=16,validation_data=(x_test_single,y_test_single),callbacks=[tensorboard_callback])\n",
        "\n",
        "# This will print a dictionary object, now you need to grab the metrics / score you're looking for\n",
        "print(history.history) \n",
        "\n",
        "best_score = max(history.history['val_accuracy'])\n",
        "\n",
        "print(f\"Best Validation score is: {best_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70JHB70fIk2o",
        "outputId": "ef92dd92-8df0-43c2-cb17-5182931f0415"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "350/350 [==============================] - 12s 24ms/step - loss: 0.1283 - val_loss: 0.1089\n",
            "Epoch 2/100\n",
            "350/350 [==============================] - 9s 26ms/step - loss: 0.0057 - val_loss: 0.0503\n",
            "Epoch 3/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0046 - val_loss: 0.0159\n",
            "Epoch 4/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0042 - val_loss: 0.0389\n",
            "Epoch 5/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0039 - val_loss: 0.0273\n",
            "Epoch 6/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0038 - val_loss: 0.0610\n",
            "Epoch 7/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0037 - val_loss: 0.0157\n",
            "Epoch 8/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0035 - val_loss: 0.0494\n",
            "Epoch 9/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0036 - val_loss: 0.0171\n",
            "Epoch 10/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0034 - val_loss: 0.0184\n",
            "Epoch 11/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0035 - val_loss: 0.0919\n",
            "Epoch 12/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0034 - val_loss: 0.0414\n",
            "Epoch 13/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0035 - val_loss: 0.0208\n",
            "Epoch 14/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0033 - val_loss: 0.0600\n",
            "Epoch 15/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0034 - val_loss: 0.0281\n",
            "Epoch 16/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0034 - val_loss: 0.0242\n",
            "Epoch 17/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0034 - val_loss: 0.0231\n",
            "Epoch 18/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0175\n",
            "Epoch 19/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0035 - val_loss: 0.0567\n",
            "Epoch 20/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0232\n",
            "Epoch 21/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0032 - val_loss: 0.0515\n",
            "Epoch 22/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0033 - val_loss: 0.0145\n",
            "Epoch 23/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0034 - val_loss: 0.0618\n",
            "Epoch 24/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0032 - val_loss: 0.0474\n",
            "Epoch 25/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0033 - val_loss: 0.0328\n",
            "Epoch 26/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0032 - val_loss: 0.1075\n",
            "Epoch 27/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0033 - val_loss: 0.0139\n",
            "Epoch 28/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0145\n",
            "Epoch 29/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0033 - val_loss: 0.0149\n",
            "Epoch 30/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0032 - val_loss: 0.0279\n",
            "Epoch 31/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0030 - val_loss: 0.0144\n",
            "Epoch 32/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0127\n",
            "Epoch 33/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0034 - val_loss: 0.0130\n",
            "Epoch 34/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0424\n",
            "Epoch 35/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0032 - val_loss: 0.0622\n",
            "Epoch 36/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0033 - val_loss: 0.0134\n",
            "Epoch 37/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0031 - val_loss: 0.0123\n",
            "Epoch 38/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0032 - val_loss: 0.0157\n",
            "Epoch 39/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0031 - val_loss: 0.0151\n",
            "Epoch 40/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0031 - val_loss: 0.0143\n",
            "Epoch 41/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0029 - val_loss: 0.0120\n",
            "Epoch 42/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0032 - val_loss: 0.0168\n",
            "Epoch 43/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0032 - val_loss: 0.0234\n",
            "Epoch 44/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0030 - val_loss: 0.0187\n",
            "Epoch 45/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0031 - val_loss: 0.0262\n",
            "Epoch 46/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0121\n",
            "Epoch 47/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0031 - val_loss: 0.0170\n",
            "Epoch 48/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0031 - val_loss: 0.0120\n",
            "Epoch 49/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0121\n",
            "Epoch 50/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0124\n",
            "Epoch 51/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0033 - val_loss: 0.0197\n",
            "Epoch 52/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0032 - val_loss: 0.0129\n",
            "Epoch 53/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0332\n",
            "Epoch 54/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0031 - val_loss: 0.0586\n",
            "Epoch 55/100\n",
            "350/350 [==============================] - 6s 17ms/step - loss: 0.0031 - val_loss: 0.0454\n",
            "Epoch 56/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0029 - val_loss: 0.0154\n",
            "Epoch 57/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0031 - val_loss: 0.0204\n",
            "Epoch 58/100\n",
            "350/350 [==============================] - 6s 17ms/step - loss: 0.0031 - val_loss: 0.0217\n",
            "Epoch 59/100\n",
            "350/350 [==============================] - 6s 17ms/step - loss: 0.0029 - val_loss: 0.0444\n",
            "Epoch 60/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0030 - val_loss: 0.0268\n",
            "Epoch 61/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0369\n",
            "Epoch 62/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0031 - val_loss: 0.0164\n",
            "Epoch 63/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0808\n",
            "Epoch 64/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0031 - val_loss: 0.0611\n",
            "Epoch 65/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0029 - val_loss: 0.0195\n",
            "Epoch 66/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0029 - val_loss: 0.0709\n",
            "Epoch 67/100\n",
            "350/350 [==============================] - 6s 17ms/step - loss: 0.0029 - val_loss: 0.0141\n",
            "Epoch 68/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0030 - val_loss: 0.1045\n",
            "Epoch 69/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0029 - val_loss: 0.0370\n",
            "Epoch 70/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0626\n",
            "Epoch 71/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0030 - val_loss: 0.0272\n",
            "Epoch 72/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0945\n",
            "Epoch 73/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0029 - val_loss: 0.0161\n",
            "Epoch 74/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0029 - val_loss: 0.0292\n",
            "Epoch 75/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0028 - val_loss: 0.0272\n",
            "Epoch 76/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0427\n",
            "Epoch 77/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0031 - val_loss: 0.0600\n",
            "Epoch 78/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0724\n",
            "Epoch 79/100\n",
            "350/350 [==============================] - 5s 15ms/step - loss: 0.0029 - val_loss: 0.0323\n",
            "Epoch 80/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0028 - val_loss: 0.0625\n",
            "Epoch 81/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0028 - val_loss: 0.0779\n",
            "Epoch 82/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0331\n",
            "Epoch 83/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0028 - val_loss: 0.0288\n",
            "Epoch 84/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0632\n",
            "Epoch 85/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0481\n",
            "Epoch 86/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0028 - val_loss: 0.0555\n",
            "Epoch 87/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0029 - val_loss: 0.0840\n",
            "Epoch 88/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0030 - val_loss: 0.0572\n",
            "Epoch 89/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0029 - val_loss: 0.0343\n",
            "Epoch 90/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0297\n",
            "Epoch 91/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0027 - val_loss: 0.0768\n",
            "Epoch 92/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0687\n",
            "Epoch 93/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0029 - val_loss: 0.0599\n",
            "Epoch 94/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0515\n",
            "Epoch 95/100\n",
            "350/350 [==============================] - 6s 16ms/step - loss: 0.0030 - val_loss: 0.0611\n",
            "Epoch 96/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0733\n",
            "Epoch 97/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0029 - val_loss: 0.1005\n",
            "Epoch 98/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0029 - val_loss: 0.0365\n",
            "Epoch 99/100\n",
            "350/350 [==============================] - 5s 16ms/step - loss: 0.0029 - val_loss: 0.0747\n",
            "Epoch 100/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0028 - val_loss: 0.0351\n",
            "{'loss': [0.24780696630477905, 0.04804559797048569, 0.01933913491666317, 0.010211944580078125, 0.006804903037846088, 0.005762386601418257, 0.004972095135599375, 0.004726823419332504, 0.004339788109064102, 0.0040133981965482235, 0.004187490325421095, 0.004298449959605932, 0.0041707782074809074, 0.003919088281691074, 0.003968916367739439, 0.00404971931129694, 0.0038405281957238913, 0.0041285064071416855, 0.003681407542899251, 0.003912126179784536, 0.003666473086923361, 0.003841744503006339, 0.0037956309970468283, 0.003750966629013419, 0.0037407809868454933, 0.003654424799606204, 0.004140788223594427, 0.0037182290107011795, 0.0035009863786399364, 0.0038232251536101103, 0.003756418591365218, 0.0036604979541152716, 0.0036799428053200245, 0.003756870748475194, 0.003643561154603958, 0.0038945963606238365, 0.0037564414087682962, 0.003553747432306409, 0.0037396238185465336, 0.0033979981672018766, 0.003578850068151951, 0.003589326748624444, 0.003773066448047757, 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            "Best Validation score is: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize model loss\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(history_LSTM.history['loss'])\n",
        "plt.plot(history_LSTM.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "KITeuIEqImRD",
        "outputId": "dfef826d-aeca-4ffd-b5c1-d77e8b81a921"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_lstm=LSTM_model.predict(x_test_single)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUJ8EAL6K6_q",
        "outputId": "b85d0a4e-e50d-44b2-c319-b72273f1502a"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#0.01475832806648973\n",
        "\n",
        "mse_LSTM= mean_squared_error(y_test_single, pred_lstm)\n",
        "mse_LSTM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2oiDNc8LBBK",
        "outputId": "82276d18-1cd5-44e4-98b4-5ef6e95cdd18"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03469288917288377"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_LSTM_inv=scaler.inverse_transform(pred_lstm)\n",
        "\n",
        "y_test_single_inv=scaler.inverse_transform(y_test_single)"
      ],
      "metadata": {
        "id": "3Iz1AXp3LCVI"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (15,10))\n",
        "plt.plot(predict_LSTM_inv, color='b', label='Real ')\n",
        "plt.plot(y_test_single_inv, color='r', label='Predicted ')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "W3UantYXLFqn",
        "outputId": "3bcb4390-9035-4c84-8662-5957c5b364fd"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3974ab3fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GRU"
      ],
      "metadata": {
        "id": "JJj6lyawLK0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "GRU_model = Sequential()\n",
        "GRU_model.add(GRU(50,input_shape=x_train_single.shape[-2:],activation='relu',return_sequences=True,kernel_regularizer='l2'))\n",
        "GRU_model.add(Dropout(0.3))\n",
        "\n",
        "GRU_model.add(GRU(50,activation='relu',kernel_regularizer='l2'))\n",
        "GRU_model.add(Dropout(0.3))\n",
        "\n",
        "GRU_model.add(Dense(n_step,activation='selu'))\n",
        "GRU_model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fq5lmBQ4LHGA",
        "outputId": "ec88ee38-f450-4344-b507-d5bddfe2824f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_GRU=GRU_model.fit(x_train_single,y_train_single,\n",
        "                                            epochs=100,batch_size=16,validation_data=(x_test_single,y_test_single))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCPP2GriLO6i",
        "outputId": "32a1a222-b5e9-4ed4-9af3-ed90a0b23d2b"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "350/350 [==============================] - 12s 19ms/step - loss: 0.1623 - val_loss: 0.0281\n",
            "Epoch 2/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0118 - val_loss: 0.0365\n",
            "Epoch 3/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0083 - val_loss: 0.0377\n",
            "Epoch 4/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0071 - val_loss: 0.0297\n",
            "Epoch 5/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0069 - val_loss: 0.0403\n",
            "Epoch 6/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0064 - val_loss: 0.0270\n",
            "Epoch 7/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0067 - val_loss: 0.0485\n",
            "Epoch 8/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0063 - val_loss: 0.0172\n",
            "Epoch 9/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0060 - val_loss: 0.0423\n",
            "Epoch 10/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0059 - val_loss: 0.0628\n",
            "Epoch 11/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0059 - val_loss: 0.0270\n",
            "Epoch 12/100\n",
            "350/350 [==============================] - 8s 23ms/step - loss: 0.0060 - val_loss: 0.0242\n",
            "Epoch 13/100\n",
            "350/350 [==============================] - 6s 19ms/step - loss: 0.0060 - val_loss: 0.0211\n",
            "Epoch 14/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0059 - val_loss: 0.0342\n",
            "Epoch 15/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0356\n",
            "Epoch 16/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0057 - val_loss: 0.0151\n",
            "Epoch 17/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0058 - val_loss: 0.0190\n",
            "Epoch 18/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0057 - val_loss: 0.0389\n",
            "Epoch 19/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0057 - val_loss: 0.0190\n",
            "Epoch 20/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0059 - val_loss: 0.0155\n",
            "Epoch 21/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0055 - val_loss: 0.0294\n",
            "Epoch 22/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0058 - val_loss: 0.0181\n",
            "Epoch 23/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0358\n",
            "Epoch 24/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0059 - val_loss: 0.0229\n",
            "Epoch 25/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0373\n",
            "Epoch 26/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0055 - val_loss: 0.0300\n",
            "Epoch 27/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0053 - val_loss: 0.0169\n",
            "Epoch 28/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0056 - val_loss: 0.0157\n",
            "Epoch 29/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0057 - val_loss: 0.0282\n",
            "Epoch 30/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0060 - val_loss: 0.1093\n",
            "Epoch 31/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0054 - val_loss: 0.0586\n",
            "Epoch 32/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0052 - val_loss: 0.0512\n",
            "Epoch 33/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0186\n",
            "Epoch 34/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0054 - val_loss: 0.0172\n",
            "Epoch 35/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0159\n",
            "Epoch 36/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0149\n",
            "Epoch 37/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0521\n",
            "Epoch 38/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0057 - val_loss: 0.0151\n",
            "Epoch 39/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0054 - val_loss: 0.0273\n",
            "Epoch 40/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0056 - val_loss: 0.0572\n",
            "Epoch 41/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0052 - val_loss: 0.0189\n",
            "Epoch 42/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0054 - val_loss: 0.0157\n",
            "Epoch 43/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0057 - val_loss: 0.0209\n",
            "Epoch 44/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0054 - val_loss: 0.0180\n",
            "Epoch 45/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0053 - val_loss: 0.0307\n",
            "Epoch 46/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0053 - val_loss: 0.0323\n",
            "Epoch 47/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0054 - val_loss: 0.0355\n",
            "Epoch 48/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0149\n",
            "Epoch 49/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0054 - val_loss: 0.0150\n",
            "Epoch 50/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0295\n",
            "Epoch 51/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0058 - val_loss: 0.0196\n",
            "Epoch 52/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0054 - val_loss: 0.0365\n",
            "Epoch 53/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0169\n",
            "Epoch 54/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0053 - val_loss: 0.0248\n",
            "Epoch 55/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0180\n",
            "Epoch 56/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0058 - val_loss: 0.0216\n",
            "Epoch 57/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0344\n",
            "Epoch 58/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0053 - val_loss: 0.0193\n",
            "Epoch 59/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0055 - val_loss: 0.0421\n",
            "Epoch 60/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0053 - val_loss: 0.0465\n",
            "Epoch 61/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0052 - val_loss: 0.0290\n",
            "Epoch 62/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0053 - val_loss: 0.0500\n",
            "Epoch 63/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0052 - val_loss: 0.0467\n",
            "Epoch 64/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0054 - val_loss: 0.0306\n",
            "Epoch 65/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0266\n",
            "Epoch 66/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0052 - val_loss: 0.0222\n",
            "Epoch 67/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0283\n",
            "Epoch 68/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0052 - val_loss: 0.0176\n",
            "Epoch 69/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0053 - val_loss: 0.0236\n",
            "Epoch 70/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0337\n",
            "Epoch 71/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0051 - val_loss: 0.0276\n",
            "Epoch 72/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0052 - val_loss: 0.0451\n",
            "Epoch 73/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0052 - val_loss: 0.0602\n",
            "Epoch 74/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0054 - val_loss: 0.0417\n",
            "Epoch 75/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0053 - val_loss: 0.0606\n",
            "Epoch 76/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0057 - val_loss: 0.0352\n",
            "Epoch 77/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0052 - val_loss: 0.0150\n",
            "Epoch 78/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0051 - val_loss: 0.0240\n",
            "Epoch 79/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0050 - val_loss: 0.0428\n",
            "Epoch 80/100\n",
            "350/350 [==============================] - 6s 19ms/step - loss: 0.0055 - val_loss: 0.0234\n",
            "Epoch 81/100\n",
            "350/350 [==============================] - 8s 21ms/step - loss: 0.0052 - val_loss: 0.0161\n",
            "Epoch 82/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0054 - val_loss: 0.0326\n",
            "Epoch 83/100\n",
            "350/350 [==============================] - 7s 20ms/step - loss: 0.0050 - val_loss: 0.0346\n",
            "Epoch 84/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0051 - val_loss: 0.0341\n",
            "Epoch 85/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0050 - val_loss: 0.0445\n",
            "Epoch 86/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0228\n",
            "Epoch 87/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0054 - val_loss: 0.0282\n",
            "Epoch 88/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0053 - val_loss: 0.0202\n",
            "Epoch 89/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0277\n",
            "Epoch 90/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0050 - val_loss: 0.0307\n",
            "Epoch 91/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0052 - val_loss: 0.0376\n",
            "Epoch 92/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0054 - val_loss: 0.0305\n",
            "Epoch 93/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0049 - val_loss: 0.0500\n",
            "Epoch 94/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0053 - val_loss: 0.0270\n",
            "Epoch 95/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0053 - val_loss: 0.0410\n",
            "Epoch 96/100\n",
            "350/350 [==============================] - 8s 22ms/step - loss: 0.0051 - val_loss: 0.0264\n",
            "Epoch 97/100\n",
            "350/350 [==============================] - 7s 19ms/step - loss: 0.0053 - val_loss: 0.0313\n",
            "Epoch 98/100\n",
            "350/350 [==============================] - 7s 21ms/step - loss: 0.0057 - val_loss: 0.0559\n",
            "Epoch 99/100\n",
            "350/350 [==============================] - 6s 18ms/step - loss: 0.0053 - val_loss: 0.0400\n",
            "Epoch 100/100\n",
            "350/350 [==============================] - 11s 32ms/step - loss: 0.0055 - val_loss: 0.0206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(history_GRU.history['loss'])\n",
        "plt.plot(history_GRU.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Ee0BxptNLQZn",
        "outputId": "f3354e2a-353e-4b9c-a2ca-c590117e53b9"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_GRU=GRU_model.predict(x_test_single)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anun9oVTOJJi",
        "outputId": "b43c01ce-d63e-4e9b-84f2-f18cd34f277c"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 1s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_GRU= mean_squared_error(y_test_single, predict_GRU)\n",
        "mse_GRU"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sgy0roGEOKt9",
        "outputId": "0434e3bc-8af8-4b3c-af93-f598dc17d801"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.019716882253307432"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_GRU_inv=scaler.inverse_transform(predict_GRU)"
      ],
      "metadata": {
        "id": "adO4xxrkOMJK"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (15,10))\n",
        "plt.plot(predict_GRU_inv, color='b', label='Real ')\n",
        "plt.plot(y_test_single_inv, color='r', label='Predicted ')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "Fm3zM8QeOPmN",
        "outputId": "a54d5dbb-c24f-4e25-ab39-d77877668b7a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f39748ccfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "248IINz7OROs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}