Stock_Price_Prediction_and_Forecasting_with_LSTM.ipynb

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        "# Stock Price Prediction and Forecasting with LSTM\n",
        "The dataset used in this notebook can be found here: https://finance.yahoo.com/quote/MSFT/history/. I used the maximum time period Mar 13, 1986 - Oct 31, 2023."
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        "## Preparing the data"
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              "            Date        Open        High         Low       Close   Adj Close  \\\n",
              "0     1986-03-13    0.088542    0.101563    0.088542    0.097222    0.060396   \n",
              "1     1986-03-14    0.097222    0.102431    0.097222    0.100694    0.062553   \n",
              "2     1986-03-17    0.100694    0.103299    0.100694    0.102431    0.063632   \n",
              "3     1986-03-18    0.102431    0.103299    0.098958    0.099826    0.062014   \n",
              "4     1986-03-19    0.099826    0.100694    0.097222    0.098090    0.060936   \n",
              "...          ...         ...         ...         ...         ...         ...   \n",
              "9480  2023-10-24  331.299988  331.839996  327.600006  330.529999  330.529999   \n",
              "9481  2023-10-25  345.019989  346.200012  337.619995  340.670013  340.670013   \n",
              "9482  2023-10-26  340.540009  341.630005  326.940002  327.890015  327.890015   \n",
              "9483  2023-10-27  330.429993  336.720001  328.399994  329.809998  329.809998   \n",
              "9484  2023-10-30  333.410004  339.450012  331.829987  337.309998  337.309998   \n",
              "\n",
              "          Volume  \n",
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          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('MSFT.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['Date', 'Close']]\n",
        "df"
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              "            Date       Close\n",
              "0     1986-03-13    0.097222\n",
              "1     1986-03-14    0.100694\n",
              "2     1986-03-17    0.102431\n",
              "3     1986-03-18    0.099826\n",
              "4     1986-03-19    0.098090\n",
              "...          ...         ...\n",
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              "9484  2023-10-30  337.309998\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>9480</th>\n",
              "      <td>2023-10-24</td>\n",
              "      <td>330.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9481</th>\n",
              "      <td>2023-10-25</td>\n",
              "      <td>340.670013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9482</th>\n",
              "      <td>2023-10-26</td>\n",
              "      <td>327.890015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9483</th>\n",
              "      <td>2023-10-27</td>\n",
              "      <td>329.809998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9484</th>\n",
              "      <td>2023-10-30</td>\n",
              "      <td>337.309998</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9485 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fa8b1054-2e90-4380-bfcb-6bcac0c2e849 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-fa8b1054-2e90-4380-bfcb-6bcac0c2e849');\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",
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              "\n",
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              "            title=\"Suggest charts.\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf1c2ff6-0667-4c40-99fd-6dc916ea9a40 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] # Want this as DateTime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwuU3lgiiV2R",
        "outputId": "d6aa70d1-2cb5-427c-a3bb-4b9ff4052334"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1986-03-13\n",
              "1       1986-03-14\n",
              "2       1986-03-17\n",
              "3       1986-03-18\n",
              "4       1986-03-19\n",
              "           ...    \n",
              "9480    2023-10-24\n",
              "9481    2023-10-25\n",
              "9482    2023-10-26\n",
              "9483    2023-10-27\n",
              "9484    2023-10-30\n",
              "Name: Date, Length: 9485, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "\n",
        "def str_to_datetime(s):\n",
        "  split = s.split('-')\n",
        "  year, month, day = int(split[0]), int(split[1]), int(split[2])\n",
        "  return datetime.datetime(year=year, month=month, day=day)\n",
        "\n",
        "datetime_object = str_to_datetime('1986-03-19')\n",
        "datetime_object"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu1hwGSLifNI",
        "outputId": "1a8af144-6eb1-44c0-ef60-ef50083729a0"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(1986, 3, 19, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = df['Date'].apply(str_to_datetime)\n",
        "df['Date']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vh01n1t_inFw",
        "outputId": "a6b2310f-4b9b-43df-d2df-746904a7ea86"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-71-f6fc52bb0fa5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Date'] = df['Date'].apply(str_to_datetime)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1986-03-13\n",
              "1      1986-03-14\n",
              "2      1986-03-17\n",
              "3      1986-03-18\n",
              "4      1986-03-19\n",
              "          ...    \n",
              "9480   2023-10-24\n",
              "9481   2023-10-25\n",
              "9482   2023-10-26\n",
              "9483   2023-10-27\n",
              "9484   2023-10-30\n",
              "Name: Date, Length: 9485, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = df.pop('Date')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "YjcDkWRKi3xQ",
        "outputId": "083d1e18-9db8-4062-be80-7829615e545a"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Close\n",
              "Date                  \n",
              "1986-03-13    0.097222\n",
              "1986-03-14    0.100694\n",
              "1986-03-17    0.102431\n",
              "1986-03-18    0.099826\n",
              "1986-03-19    0.098090\n",
              "...                ...\n",
              "2023-10-24  330.529999\n",
              "2023-10-25  340.670013\n",
              "2023-10-26  327.890015\n",
              "2023-10-27  329.809998\n",
              "2023-10-30  337.309998\n",
              "\n",
              "[9485 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5f96778-735f-41bb-bb12-5d6d99c8d32c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "      <th>Date</th>\n",
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              "      <th>1986-03-13</th>\n",
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              "      <td>0.102431</td>\n",
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              "    <tr>\n",
              "      <th>1986-03-18</th>\n",
              "      <td>0.099826</td>\n",
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              "      <th>1986-03-19</th>\n",
              "      <td>0.098090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2023-10-24</th>\n",
              "      <td>330.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-25</th>\n",
              "      <td>340.670013</td>\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "        document.querySelector('#df-d5f96778-735f-41bb-bb12-5d6d99c8d32c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d5f96778-735f-41bb-bb12-5d6d99c8d32c');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-72961dea-125d-46f4-a7cf-517492f31e90 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df.index, df['Close'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "aUBEB80fjBGN",
        "outputId": "7bc23bcf-7618-4955-8afc-ec97d4974564"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7dbf7a462200>]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transforming the data to a supervised learning problem\n",
        "import numpy as np\n",
        "\n",
        "def df_to_windowed_df(dataframe, first_date_str, last_date_str, n=3):\n",
        "  first_date = str_to_datetime(first_date_str)\n",
        "  last_date  = str_to_datetime(last_date_str)\n",
        "\n",
        "  target_date = first_date\n",
        "\n",
        "  dates = []\n",
        "  X, Y = [], []\n",
        "\n",
        "  last_time = False\n",
        "  while True:\n",
        "    df_subset = dataframe.loc[:target_date].tail(n+1)\n",
        "\n",
        "    if len(df_subset) != n+1:\n",
        "      print(f'Error: Window of size {n} is too large for date {target_date}')\n",
        "      return\n",
        "\n",
        "    values = df_subset['Close'].to_numpy()\n",
        "    x, y = values[:-1], values[-1]\n",
        "\n",
        "    dates.append(target_date)\n",
        "    X.append(x)\n",
        "    Y.append(y)\n",
        "\n",
        "    next_week = dataframe.loc[target_date:target_date+datetime.timedelta(days=7)]\n",
        "    next_datetime_str = str(next_week.head(2).tail(1).index.values[0])\n",
        "    next_date_str = next_datetime_str.split('T')[0]\n",
        "    year_month_day = next_date_str.split('-')\n",
        "    year, month, day = year_month_day\n",
        "    next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))\n",
        "\n",
        "    if last_time:\n",
        "      break\n",
        "\n",
        "    target_date = next_date\n",
        "\n",
        "    if target_date == last_date:\n",
        "      last_time = True\n",
        "\n",
        "  ret_df = pd.DataFrame({})\n",
        "  ret_df['Target Date'] = dates\n",
        "\n",
        "  X = np.array(X)\n",
        "  for i in range(0, n):\n",
        "    X[:, i]\n",
        "    ret_df[f'Target-{n-i}'] = X[:, i]\n",
        "\n",
        "  ret_df['Target'] = Y\n",
        "\n",
        "  return ret_df\n",
        "\n",
        "windowed_df = df_to_windowed_df(df,\n",
        "                                '1986-03-18', # Earliest possible start date\n",
        "                                '2023-10-30',\n",
        "                                n=3)\n",
        "windowed_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Xta5HW4jjQ8n",
        "outputId": "59a913ba-3ed5-46eb-dc3c-b44459db377a"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Target Date    Target-3    Target-2    Target-1      Target\n",
              "0     1986-03-18    0.097222    0.100694    0.102431    0.099826\n",
              "1     1986-03-19    0.100694    0.102431    0.099826    0.098090\n",
              "2     1986-03-20    0.102431    0.099826    0.098090    0.095486\n",
              "3     1986-03-21    0.099826    0.098090    0.095486    0.092882\n",
              "4     1986-03-24    0.098090    0.095486    0.092882    0.090278\n",
              "...          ...         ...         ...         ...         ...\n",
              "9477  2023-10-24  331.320007  326.670013  329.320007  330.529999\n",
              "9478  2023-10-25  326.670013  329.320007  330.529999  340.670013\n",
              "9479  2023-10-26  329.320007  330.529999  340.670013  327.890015\n",
              "9480  2023-10-27  330.529999  340.670013  327.890015  329.809998\n",
              "9481  2023-10-30  340.670013  327.890015  329.809998  337.309998\n",
              "\n",
              "[9482 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1870897-c8ae-461d-b818-e7c8a06de0f7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Target Date</th>\n",
              "      <th>Target-3</th>\n",
              "      <th>Target-2</th>\n",
              "      <th>Target-1</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1986-03-18</td>\n",
              "      <td>0.097222</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.099826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1986-03-19</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.099826</td>\n",
              "      <td>0.098090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1986-03-20</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.099826</td>\n",
              "      <td>0.098090</td>\n",
              "      <td>0.095486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1986-03-21</td>\n",
              "      <td>0.099826</td>\n",
              "      <td>0.098090</td>\n",
              "      <td>0.095486</td>\n",
              "      <td>0.092882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1986-03-24</td>\n",
              "      <td>0.098090</td>\n",
              "      <td>0.095486</td>\n",
              "      <td>0.092882</td>\n",
              "      <td>0.090278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9477</th>\n",
              "      <td>2023-10-24</td>\n",
              "      <td>331.320007</td>\n",
              "      <td>326.670013</td>\n",
              "      <td>329.320007</td>\n",
              "      <td>330.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9478</th>\n",
              "      <td>2023-10-25</td>\n",
              "      <td>326.670013</td>\n",
              "      <td>329.320007</td>\n",
              "      <td>330.529999</td>\n",
              "      <td>340.670013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9479</th>\n",
              "      <td>2023-10-26</td>\n",
              "      <td>329.320007</td>\n",
              "      <td>330.529999</td>\n",
              "      <td>340.670013</td>\n",
              "      <td>327.890015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9480</th>\n",
              "      <td>2023-10-27</td>\n",
              "      <td>330.529999</td>\n",
              "      <td>340.670013</td>\n",
              "      <td>327.890015</td>\n",
              "      <td>329.809998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9481</th>\n",
              "      <td>2023-10-30</td>\n",
              "      <td>340.670013</td>\n",
              "      <td>327.890015</td>\n",
              "      <td>329.809998</td>\n",
              "      <td>337.309998</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9482 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1870897-c8ae-461d-b818-e7c8a06de0f7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1870897-c8ae-461d-b818-e7c8a06de0f7 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-a1870897-c8ae-461d-b818-e7c8a06de0f7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-85eb3db6-59ca-4a13-a8b9-edaadc5d0ea0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85eb3db6-59ca-4a13-a8b9-edaadc5d0ea0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85eb3db6-59ca-4a13-a8b9-edaadc5d0ea0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def windowed_df_to_date_X_y(windowed_dataframe):\n",
        "  df_as_np = windowed_dataframe.to_numpy()\n",
        "\n",
        "  dates = df_as_np[:, 0]\n",
        "\n",
        "  middle_matrix = df_as_np[:, 1:-1]\n",
        "  X = middle_matrix.reshape((len(dates), middle_matrix.shape[1], 1))\n",
        "\n",
        "  Y = df_as_np[:, -1]\n",
        "\n",
        "  return dates, X.astype(np.float32), Y.astype(np.float32)\n",
        "\n",
        "dates, X, y = windowed_df_to_date_X_y(windowed_df)\n",
        "\n",
        "dates.shape, X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrdwzu8FkYN0",
        "outputId": "6f23120b-3753-4e7b-f800-27e8712ad039"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9482,), (9482, 3, 1), (9482,))"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q_80 = int(len(dates) * .8)\n",
        "q_90 = int(len(dates) * .9)\n",
        "\n",
        "dates_train, X_train, y_train = dates[:q_80], X[:q_80], y[:q_80]\n",
        "\n",
        "dates_val, X_val, y_val = dates[q_80:q_90], X[q_80:q_90], y[q_80:q_90]\n",
        "dates_test, X_test, y_test = dates[q_90:], X[q_90:], y[q_90:]\n",
        "\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, y_test)\n",
        "\n",
        "plt.legend(['Train', 'Validation', 'Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "fv7IWeR8krQQ",
        "outputId": "448ddb15-1957-4bf2-d8e5-3cd2cfff6613"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf89972980>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating the LSTM model"
      ],
      "metadata": {
        "id": "dg8OssHFl35W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = Sequential([layers.Input((3, 1)),\n",
        "                    layers.LSTM(64),\n",
        "                    layers.Dense(32, activation='relu'),\n",
        "                    layers.Dense(32, activation='relu'),\n",
        "                    layers.Dense(1)])\n",
        "\n",
        "model.compile(loss='mse',\n",
        "              optimizer=Adam(learning_rate=0.001),\n",
        "              metrics=['mean_absolute_error'])\n",
        "\n",
        "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKwpN1sVktgc",
        "outputId": "859004b4-ddd7-4cc6-973c-b9f6ade1a432"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "238/238 [==============================] - 5s 6ms/step - loss: 91.7712 - mean_absolute_error: 4.1777 - val_loss: 2559.4844 - val_mean_absolute_error: 39.7908\n",
            "Epoch 2/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3563 - mean_absolute_error: 0.3467 - val_loss: 2360.2537 - val_mean_absolute_error: 37.5466\n",
            "Epoch 3/100\n",
            "238/238 [==============================] - 2s 6ms/step - loss: 0.3218 - mean_absolute_error: 0.3328 - val_loss: 2278.9517 - val_mean_absolute_error: 36.5502\n",
            "Epoch 4/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3229 - mean_absolute_error: 0.3327 - val_loss: 2247.7500 - val_mean_absolute_error: 36.1835\n",
            "Epoch 5/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3602 - mean_absolute_error: 0.3541 - val_loss: 2243.4321 - val_mean_absolute_error: 36.1677\n",
            "Epoch 6/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3589 - mean_absolute_error: 0.3535 - val_loss: 2228.2295 - val_mean_absolute_error: 35.9076\n",
            "Epoch 7/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3179 - mean_absolute_error: 0.3321 - val_loss: 2214.5972 - val_mean_absolute_error: 35.7096\n",
            "Epoch 8/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3070 - mean_absolute_error: 0.3196 - val_loss: 2224.6409 - val_mean_absolute_error: 35.9804\n",
            "Epoch 9/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3397 - mean_absolute_error: 0.3448 - val_loss: 2236.1243 - val_mean_absolute_error: 35.9452\n",
            "Epoch 10/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3195 - mean_absolute_error: 0.3307 - val_loss: 2211.7986 - val_mean_absolute_error: 35.7583\n",
            "Epoch 11/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3387 - mean_absolute_error: 0.3526 - val_loss: 2200.2219 - val_mean_absolute_error: 35.5684\n",
            "Epoch 12/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3534 - mean_absolute_error: 0.3580 - val_loss: 2203.8225 - val_mean_absolute_error: 35.7227\n",
            "Epoch 13/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3180 - mean_absolute_error: 0.3284 - val_loss: 2165.4944 - val_mean_absolute_error: 35.0436\n",
            "Epoch 14/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3367 - mean_absolute_error: 0.3449 - val_loss: 2170.0405 - val_mean_absolute_error: 35.0868\n",
            "Epoch 15/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3496 - mean_absolute_error: 0.3550 - val_loss: 2188.1033 - val_mean_absolute_error: 35.3331\n",
            "Epoch 16/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3150 - mean_absolute_error: 0.3302 - val_loss: 2192.3201 - val_mean_absolute_error: 35.4035\n",
            "Epoch 17/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3433 - mean_absolute_error: 0.3495 - val_loss: 2169.4001 - val_mean_absolute_error: 35.0746\n",
            "Epoch 18/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3203 - mean_absolute_error: 0.3319 - val_loss: 2110.6306 - val_mean_absolute_error: 34.4008\n",
            "Epoch 19/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3607 - mean_absolute_error: 0.3486 - val_loss: 2110.1584 - val_mean_absolute_error: 34.3937\n",
            "Epoch 20/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3188 - mean_absolute_error: 0.3324 - val_loss: 2126.4338 - val_mean_absolute_error: 34.7243\n",
            "Epoch 21/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3324 - mean_absolute_error: 0.3419 - val_loss: 2133.7473 - val_mean_absolute_error: 34.6122\n",
            "Epoch 22/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3783 - mean_absolute_error: 0.3630 - val_loss: 2167.8916 - val_mean_absolute_error: 35.1764\n",
            "Epoch 23/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3195 - mean_absolute_error: 0.3388 - val_loss: 2123.2419 - val_mean_absolute_error: 34.4606\n",
            "Epoch 24/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3278 - mean_absolute_error: 0.3394 - val_loss: 2159.1541 - val_mean_absolute_error: 35.1207\n",
            "Epoch 25/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3262 - mean_absolute_error: 0.3348 - val_loss: 2153.8708 - val_mean_absolute_error: 34.9031\n",
            "Epoch 26/100\n",
            "238/238 [==============================] - 2s 6ms/step - loss: 0.3212 - mean_absolute_error: 0.3384 - val_loss: 2112.4102 - val_mean_absolute_error: 34.3842\n",
            "Epoch 27/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3230 - mean_absolute_error: 0.3404 - val_loss: 2147.2898 - val_mean_absolute_error: 34.9266\n",
            "Epoch 28/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3232 - mean_absolute_error: 0.3339 - val_loss: 2135.6948 - val_mean_absolute_error: 34.6398\n",
            "Epoch 29/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3194 - mean_absolute_error: 0.3352 - val_loss: 2145.1538 - val_mean_absolute_error: 34.7452\n",
            "Epoch 30/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3406 - mean_absolute_error: 0.3503 - val_loss: 2172.7454 - val_mean_absolute_error: 35.1921\n",
            "Epoch 31/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.2967 - mean_absolute_error: 0.3170 - val_loss: 2149.5022 - val_mean_absolute_error: 34.7941\n",
            "Epoch 32/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3144 - mean_absolute_error: 0.3332 - val_loss: 2148.3044 - val_mean_absolute_error: 35.1919\n",
            "Epoch 33/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3196 - mean_absolute_error: 0.3336 - val_loss: 2107.7329 - val_mean_absolute_error: 34.5571\n",
            "Epoch 34/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3176 - mean_absolute_error: 0.3357 - val_loss: 2114.2168 - val_mean_absolute_error: 34.9666\n",
            "Epoch 35/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3512 - mean_absolute_error: 0.3570 - val_loss: 2103.0408 - val_mean_absolute_error: 34.4629\n",
            "Epoch 36/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3562 - mean_absolute_error: 0.3579 - val_loss: 2149.8838 - val_mean_absolute_error: 35.1362\n",
            "Epoch 37/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3153 - mean_absolute_error: 0.3327 - val_loss: 2073.4348 - val_mean_absolute_error: 34.0678\n",
            "Epoch 38/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3304 - mean_absolute_error: 0.3427 - val_loss: 2106.1528 - val_mean_absolute_error: 34.4969\n",
            "Epoch 39/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3217 - mean_absolute_error: 0.3348 - val_loss: 2089.8567 - val_mean_absolute_error: 34.2244\n",
            "Epoch 40/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3197 - mean_absolute_error: 0.3373 - val_loss: 2011.3329 - val_mean_absolute_error: 33.3529\n",
            "Epoch 41/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.4659 - mean_absolute_error: 0.3895 - val_loss: 1963.9902 - val_mean_absolute_error: 33.1535\n",
            "Epoch 42/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3224 - mean_absolute_error: 0.3329 - val_loss: 1956.7957 - val_mean_absolute_error: 33.2641\n",
            "Epoch 43/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.2944 - mean_absolute_error: 0.3116 - val_loss: 1923.8958 - val_mean_absolute_error: 32.6119\n",
            "Epoch 44/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3351 - mean_absolute_error: 0.3457 - val_loss: 1966.6880 - val_mean_absolute_error: 33.0817\n",
            "Epoch 45/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3229 - mean_absolute_error: 0.3384 - val_loss: 1971.7186 - val_mean_absolute_error: 33.2242\n",
            "Epoch 46/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3618 - mean_absolute_error: 0.3647 - val_loss: 1967.1747 - val_mean_absolute_error: 33.0978\n",
            "Epoch 47/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3260 - mean_absolute_error: 0.3394 - val_loss: 2006.4861 - val_mean_absolute_error: 33.6892\n",
            "Epoch 48/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3203 - mean_absolute_error: 0.3356 - val_loss: 1959.4359 - val_mean_absolute_error: 32.9330\n",
            "Epoch 49/100\n",
            "238/238 [==============================] - 1s 6ms/step - loss: 0.3217 - mean_absolute_error: 0.3376 - val_loss: 1943.0569 - val_mean_absolute_error: 32.8608\n",
            "Epoch 50/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3702 - mean_absolute_error: 0.3732 - val_loss: 1997.5284 - val_mean_absolute_error: 33.4837\n",
            "Epoch 51/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3266 - mean_absolute_error: 0.3377 - val_loss: 1952.1533 - val_mean_absolute_error: 33.0534\n",
            "Epoch 52/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3066 - mean_absolute_error: 0.3234 - val_loss: 1928.4552 - val_mean_absolute_error: 32.8468\n",
            "Epoch 53/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.2982 - mean_absolute_error: 0.3145 - val_loss: 1912.5176 - val_mean_absolute_error: 32.5269\n",
            "Epoch 54/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3143 - mean_absolute_error: 0.3294 - val_loss: 1948.5613 - val_mean_absolute_error: 33.2434\n",
            "Epoch 55/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3193 - mean_absolute_error: 0.3355 - val_loss: 1927.0889 - val_mean_absolute_error: 32.8603\n",
            "Epoch 56/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3143 - mean_absolute_error: 0.3318 - val_loss: 1882.9368 - val_mean_absolute_error: 32.0470\n",
            "Epoch 57/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3339 - mean_absolute_error: 0.3486 - val_loss: 1907.8169 - val_mean_absolute_error: 32.5120\n",
            "Epoch 58/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3245 - mean_absolute_error: 0.3397 - val_loss: 1899.4697 - val_mean_absolute_error: 32.3219\n",
            "Epoch 59/100\n",
            "238/238 [==============================] - 1s 6ms/step - loss: 0.3064 - mean_absolute_error: 0.3297 - val_loss: 1950.6243 - val_mean_absolute_error: 33.3183\n",
            "Epoch 60/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3807 - mean_absolute_error: 0.3740 - val_loss: 1851.2665 - val_mean_absolute_error: 32.5400\n",
            "Epoch 61/100\n",
            "238/238 [==============================] - 1s 6ms/step - loss: 0.3070 - mean_absolute_error: 0.3253 - val_loss: 1819.5164 - val_mean_absolute_error: 32.0264\n",
            "Epoch 62/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3358 - mean_absolute_error: 0.3470 - val_loss: 1774.4647 - val_mean_absolute_error: 31.2810\n",
            "Epoch 63/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3109 - mean_absolute_error: 0.3301 - val_loss: 1752.0952 - val_mean_absolute_error: 30.8323\n",
            "Epoch 64/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3149 - mean_absolute_error: 0.3285 - val_loss: 1805.8990 - val_mean_absolute_error: 31.4890\n",
            "Epoch 65/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3404 - mean_absolute_error: 0.3464 - val_loss: 1888.6641 - val_mean_absolute_error: 32.7278\n",
            "Epoch 66/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3273 - mean_absolute_error: 0.3412 - val_loss: 1832.1943 - val_mean_absolute_error: 31.8439\n",
            "Epoch 67/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3219 - mean_absolute_error: 0.3367 - val_loss: 1834.4374 - val_mean_absolute_error: 31.7030\n",
            "Epoch 68/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3237 - mean_absolute_error: 0.3334 - val_loss: 1818.9437 - val_mean_absolute_error: 31.5682\n",
            "Epoch 69/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3014 - mean_absolute_error: 0.3218 - val_loss: 1780.9065 - val_mean_absolute_error: 30.9882\n",
            "Epoch 70/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3229 - mean_absolute_error: 0.3333 - val_loss: 1832.5011 - val_mean_absolute_error: 31.6805\n",
            "Epoch 71/100\n",
            "238/238 [==============================] - 2s 8ms/step - loss: 0.3071 - mean_absolute_error: 0.3250 - val_loss: 1842.6545 - val_mean_absolute_error: 31.8702\n",
            "Epoch 72/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3044 - mean_absolute_error: 0.3218 - val_loss: 1844.4296 - val_mean_absolute_error: 31.9251\n",
            "Epoch 73/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3412 - mean_absolute_error: 0.3460 - val_loss: 1817.5725 - val_mean_absolute_error: 31.4511\n",
            "Epoch 74/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3176 - mean_absolute_error: 0.3310 - val_loss: 1765.4467 - val_mean_absolute_error: 30.9195\n",
            "Epoch 75/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3301 - mean_absolute_error: 0.3384 - val_loss: 1818.4530 - val_mean_absolute_error: 31.5767\n",
            "Epoch 76/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.2952 - mean_absolute_error: 0.3117 - val_loss: 1810.9294 - val_mean_absolute_error: 31.4233\n",
            "Epoch 77/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3135 - mean_absolute_error: 0.3323 - val_loss: 1803.8427 - val_mean_absolute_error: 31.3052\n",
            "Epoch 78/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3340 - mean_absolute_error: 0.3430 - val_loss: 1757.9113 - val_mean_absolute_error: 30.8492\n",
            "Epoch 79/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3295 - mean_absolute_error: 0.3429 - val_loss: 1828.9171 - val_mean_absolute_error: 31.7020\n",
            "Epoch 80/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3142 - mean_absolute_error: 0.3307 - val_loss: 1817.4834 - val_mean_absolute_error: 31.5924\n",
            "Epoch 81/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.2975 - mean_absolute_error: 0.3211 - val_loss: 1824.2242 - val_mean_absolute_error: 31.8468\n",
            "Epoch 82/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3185 - mean_absolute_error: 0.3340 - val_loss: 1997.9672 - val_mean_absolute_error: 34.7761\n",
            "Epoch 83/100\n",
            "238/238 [==============================] - 1s 6ms/step - loss: 0.5765 - mean_absolute_error: 0.4217 - val_loss: 1707.0496 - val_mean_absolute_error: 31.2659\n",
            "Epoch 84/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3174 - mean_absolute_error: 0.3330 - val_loss: 1733.8013 - val_mean_absolute_error: 31.6786\n",
            "Epoch 85/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3120 - mean_absolute_error: 0.3260 - val_loss: 1741.3164 - val_mean_absolute_error: 31.7841\n",
            "Epoch 86/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3004 - mean_absolute_error: 0.3153 - val_loss: 1740.9297 - val_mean_absolute_error: 31.6117\n",
            "Epoch 87/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3114 - mean_absolute_error: 0.3274 - val_loss: 1752.5392 - val_mean_absolute_error: 31.7613\n",
            "Epoch 88/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3121 - mean_absolute_error: 0.3287 - val_loss: 1724.0999 - val_mean_absolute_error: 31.2501\n",
            "Epoch 89/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3081 - mean_absolute_error: 0.3245 - val_loss: 1723.5295 - val_mean_absolute_error: 31.1907\n",
            "Epoch 90/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3343 - mean_absolute_error: 0.3496 - val_loss: 1722.1172 - val_mean_absolute_error: 31.1416\n",
            "Epoch 91/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3068 - mean_absolute_error: 0.3227 - val_loss: 1686.2450 - val_mean_absolute_error: 30.6616\n",
            "Epoch 92/100\n",
            "238/238 [==============================] - 1s 6ms/step - loss: 0.3197 - mean_absolute_error: 0.3299 - val_loss: 1695.2504 - val_mean_absolute_error: 30.7372\n",
            "Epoch 93/100\n",
            "238/238 [==============================] - 2s 7ms/step - loss: 0.3098 - mean_absolute_error: 0.3242 - val_loss: 1752.3591 - val_mean_absolute_error: 31.4857\n",
            "Epoch 94/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.3541 - mean_absolute_error: 0.3556 - val_loss: 1785.5358 - val_mean_absolute_error: 32.0501\n",
            "Epoch 95/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.2937 - mean_absolute_error: 0.3119 - val_loss: 1705.2911 - val_mean_absolute_error: 30.9486\n",
            "Epoch 96/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3120 - mean_absolute_error: 0.3289 - val_loss: 1729.0839 - val_mean_absolute_error: 31.2727\n",
            "Epoch 97/100\n",
            "238/238 [==============================] - 1s 4ms/step - loss: 0.2972 - mean_absolute_error: 0.3135 - val_loss: 1773.2681 - val_mean_absolute_error: 32.0040\n",
            "Epoch 98/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3193 - mean_absolute_error: 0.3330 - val_loss: 1785.9124 - val_mean_absolute_error: 32.1383\n",
            "Epoch 99/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3074 - mean_absolute_error: 0.3263 - val_loss: 1796.6001 - val_mean_absolute_error: 32.3279\n",
            "Epoch 100/100\n",
            "238/238 [==============================] - 1s 5ms/step - loss: 0.3189 - mean_absolute_error: 0.3273 - val_loss: 1732.4540 - val_mean_absolute_error: 31.2508\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7dbf7a1d1d80>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the LSTM model"
      ],
      "metadata": {
        "id": "EGeea4_Gl8xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_predictions = model.predict(X_train).flatten()\n",
        "\n",
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.legend(['Training Predictions', 'Training Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "hJ23UR0Tl_Lj",
        "outputId": "fddd4659-546d-44dc-cce2-936ea7b7c8a3"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "238/238 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf793662f0>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_predictions = model.predict(X_val).flatten()\n",
        "\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.legend(['Validation Predictions', 'Validation Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "mY_z6-TemCDF",
        "outputId": "d22d17c1-577a-4db4-9f3c-858649c24167"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30/30 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf79287820>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(X_test).flatten()\n",
        "\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.legend(['Testing Predictions', 'Testing Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "j26jgJchmDaP",
        "outputId": "0ab0e9dc-8b97-46fc-db44-c85d75d0a0f5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30/30 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf7a1644f0>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.legend(['Training Predictions',\n",
        "            'Training Observations',\n",
        "            'Validation Predictions',\n",
        "            'Validation Observations',\n",
        "            'Testing Predictions',\n",
        "            'Testing Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "sCK2vFuSmFNU",
        "outputId": "dabe54cd-8b6b-48da-deeb-ed879cbdac78"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf81add4b0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "from copy import deepcopy\n",
        "\n",
        "recursive_predictions = []\n",
        "recursive_dates = np.concatenate([dates_val, dates_test])\n",
        "\n",
        "for target_date in recursive_dates:\n",
        "  last_window = deepcopy(X_train[-1])\n",
        "  next_prediction = model.predict(np.array([last_window])).flatten()\n",
        "  recursive_predictions.append(next_prediction)\n",
        "  last_window[-1] = next_prediction"
      ],
      "metadata": {
        "id": "aIIxB7lgmHEk"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.plot(recursive_dates, recursive_predictions)\n",
        "plt.legend(['Training Predictions',\n",
        "            'Training Observations',\n",
        "            'Validation Predictions',\n",
        "            'Validation Observations',\n",
        "            'Testing Predictions',\n",
        "            'Testing Observations',\n",
        "            'Recursive Predictions'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KfZSWwyDmIvI",
        "outputId": "27482d4e-dbd1-42af-ee03-f06c2e9e657e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf7a417190>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing old training data to focus more on recent trends and retraining the model\n",
        "As seen in the plots before, the predictions are quite bad. One reason for this could be that we are training on a different distribution than we are testing on. It is difficult to say if and how the stock price of 1990 actually affects the stock price in 2023."
      ],
      "metadata": {
        "id": "BVI38vn9n6Df"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "windowed_df = df_to_windowed_df(df,\n",
        "                                '2021-03-25',\n",
        "                                '2023-10-30',\n",
        "                                n=3)\n",
        "dates, X, y = windowed_df_to_date_X_y(windowed_df)\n",
        "\n",
        "dates.shape, X.shape, y.shape\n",
        "\n",
        "q_80 = int(len(dates) * .8)\n",
        "q_90 = int(len(dates) * .9)\n",
        "\n",
        "dates_train, X_train, y_train = dates[:q_80], X[:q_80], y[:q_80]\n",
        "\n",
        "dates_val, X_val, y_val = dates[q_80:q_90], X[q_80:q_90], y[q_80:q_90]\n",
        "dates_test, X_test, y_test = dates[q_90:], X[q_90:], y[q_90:]\n",
        "\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, y_test)\n",
        "\n",
        "plt.legend(['Train', 'Validation', 'Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ebSLe0nRoQ9d",
        "outputId": "2fd257aa-bd4d-4578-9d8a-2a09b44b226b"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf80e275e0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5k03QB4_omX_",
        "outputId": "e5b54183-de1c-4795-8a0b-8883f244ed9d"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 22774.4258 - mean_absolute_error: 136.1501 - val_loss: 3203.0857 - val_mean_absolute_error: 54.2944\n",
            "Epoch 2/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 2306.8779 - mean_absolute_error: 40.7061 - val_loss: 2689.3428 - val_mean_absolute_error: 49.4223\n",
            "Epoch 3/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 1070.2781 - mean_absolute_error: 26.0320 - val_loss: 3935.3445 - val_mean_absolute_error: 60.6641\n",
            "Epoch 4/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 863.0887 - mean_absolute_error: 24.5633 - val_loss: 2447.9922 - val_mean_absolute_error: 46.9897\n",
            "Epoch 5/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 834.1266 - mean_absolute_error: 24.0911 - val_loss: 3050.5715 - val_mean_absolute_error: 53.2312\n",
            "Epoch 6/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 741.1934 - mean_absolute_error: 22.6437 - val_loss: 2514.4482 - val_mean_absolute_error: 47.8083\n",
            "Epoch 7/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 657.7278 - mean_absolute_error: 21.1398 - val_loss: 2440.9343 - val_mean_absolute_error: 47.1182\n",
            "Epoch 8/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 531.5240 - mean_absolute_error: 18.6771 - val_loss: 1979.9247 - val_mean_absolute_error: 42.2953\n",
            "Epoch 9/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 440.8737 - mean_absolute_error: 16.9598 - val_loss: 1831.9843 - val_mean_absolute_error: 40.8442\n",
            "Epoch 10/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 386.4019 - mean_absolute_error: 15.5240 - val_loss: 1360.3016 - val_mean_absolute_error: 34.7114\n",
            "Epoch 11/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 335.7350 - mean_absolute_error: 14.4904 - val_loss: 1273.0928 - val_mean_absolute_error: 33.4468\n",
            "Epoch 12/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 290.7275 - mean_absolute_error: 13.3458 - val_loss: 1300.2635 - val_mean_absolute_error: 34.0569\n",
            "Epoch 13/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 258.0685 - mean_absolute_error: 12.3609 - val_loss: 1164.7775 - val_mean_absolute_error: 31.9447\n",
            "Epoch 14/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 220.7171 - mean_absolute_error: 11.2948 - val_loss: 975.4952 - val_mean_absolute_error: 29.1124\n",
            "Epoch 15/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 184.4618 - mean_absolute_error: 10.4117 - val_loss: 763.2726 - val_mean_absolute_error: 25.0433\n",
            "Epoch 16/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 167.3476 - mean_absolute_error: 9.8766 - val_loss: 573.2988 - val_mean_absolute_error: 21.4299\n",
            "Epoch 17/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 138.3782 - mean_absolute_error: 8.8167 - val_loss: 594.6885 - val_mean_absolute_error: 22.2650\n",
            "Epoch 18/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 120.9628 - mean_absolute_error: 8.2993 - val_loss: 400.6804 - val_mean_absolute_error: 17.0084\n",
            "Epoch 19/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 128.1170 - mean_absolute_error: 8.9924 - val_loss: 516.4075 - val_mean_absolute_error: 20.7771\n",
            "Epoch 20/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 111.0262 - mean_absolute_error: 8.2602 - val_loss: 389.8431 - val_mean_absolute_error: 17.3657\n",
            "Epoch 21/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 91.8935 - mean_absolute_error: 7.2017 - val_loss: 337.7222 - val_mean_absolute_error: 15.7365\n",
            "Epoch 22/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 86.7468 - mean_absolute_error: 7.1625 - val_loss: 346.4710 - val_mean_absolute_error: 16.4152\n",
            "Epoch 23/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 87.4244 - mean_absolute_error: 7.2251 - val_loss: 260.6340 - val_mean_absolute_error: 13.5276\n",
            "Epoch 24/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 74.2686 - mean_absolute_error: 6.6343 - val_loss: 293.4939 - val_mean_absolute_error: 14.6403\n",
            "Epoch 25/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 68.5790 - mean_absolute_error: 6.4533 - val_loss: 229.2015 - val_mean_absolute_error: 12.7053\n",
            "Epoch 26/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 65.8966 - mean_absolute_error: 6.4379 - val_loss: 198.2132 - val_mean_absolute_error: 11.7479\n",
            "Epoch 27/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 59.7110 - mean_absolute_error: 5.9696 - val_loss: 139.4688 - val_mean_absolute_error: 9.9032\n",
            "Epoch 28/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 63.1699 - mean_absolute_error: 6.2954 - val_loss: 153.9708 - val_mean_absolute_error: 10.1956\n",
            "Epoch 29/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 62.0064 - mean_absolute_error: 6.1641 - val_loss: 124.9642 - val_mean_absolute_error: 9.1660\n",
            "Epoch 30/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 58.0000 - mean_absolute_error: 6.0779 - val_loss: 134.4586 - val_mean_absolute_error: 9.4446\n",
            "Epoch 31/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 54.7575 - mean_absolute_error: 5.8146 - val_loss: 137.7214 - val_mean_absolute_error: 9.6228\n",
            "Epoch 32/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 50.5334 - mean_absolute_error: 5.5927 - val_loss: 149.1643 - val_mean_absolute_error: 10.2110\n",
            "Epoch 33/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 51.0507 - mean_absolute_error: 5.6174 - val_loss: 131.0717 - val_mean_absolute_error: 9.4350\n",
            "Epoch 34/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 48.9180 - mean_absolute_error: 5.5030 - val_loss: 116.3295 - val_mean_absolute_error: 8.7499\n",
            "Epoch 35/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 56.9659 - mean_absolute_error: 6.0581 - val_loss: 82.9276 - val_mean_absolute_error: 7.5882\n",
            "Epoch 36/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 62.2781 - mean_absolute_error: 6.4170 - val_loss: 79.4570 - val_mean_absolute_error: 7.3934\n",
            "Epoch 37/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 56.7330 - mean_absolute_error: 6.0294 - val_loss: 82.3821 - val_mean_absolute_error: 7.2698\n",
            "Epoch 38/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 47.8629 - mean_absolute_error: 5.3952 - val_loss: 114.8732 - val_mean_absolute_error: 8.7894\n",
            "Epoch 39/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 49.3521 - mean_absolute_error: 5.5674 - val_loss: 88.9428 - val_mean_absolute_error: 7.4270\n",
            "Epoch 40/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 45.5737 - mean_absolute_error: 5.3381 - val_loss: 92.0633 - val_mean_absolute_error: 7.5695\n",
            "Epoch 41/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 47.1068 - mean_absolute_error: 5.3912 - val_loss: 96.5510 - val_mean_absolute_error: 7.8288\n",
            "Epoch 42/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 47.2659 - mean_absolute_error: 5.4636 - val_loss: 68.6851 - val_mean_absolute_error: 6.5147\n",
            "Epoch 43/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 45.8111 - mean_absolute_error: 5.3375 - val_loss: 67.3884 - val_mean_absolute_error: 6.4183\n",
            "Epoch 44/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 43.8613 - mean_absolute_error: 5.2168 - val_loss: 69.7176 - val_mean_absolute_error: 6.5573\n",
            "Epoch 45/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 43.9874 - mean_absolute_error: 5.1975 - val_loss: 70.7728 - val_mean_absolute_error: 6.5886\n",
            "Epoch 46/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 45.3707 - mean_absolute_error: 5.3529 - val_loss: 82.1689 - val_mean_absolute_error: 7.1800\n",
            "Epoch 47/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 43.6736 - mean_absolute_error: 5.1645 - val_loss: 77.7196 - val_mean_absolute_error: 6.9693\n",
            "Epoch 48/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 41.6847 - mean_absolute_error: 5.1182 - val_loss: 69.4478 - val_mean_absolute_error: 6.5249\n",
            "Epoch 49/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 43.7521 - mean_absolute_error: 5.2565 - val_loss: 80.4585 - val_mean_absolute_error: 7.0418\n",
            "Epoch 50/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 45.8713 - mean_absolute_error: 5.3862 - val_loss: 56.7403 - val_mean_absolute_error: 5.8504\n",
            "Epoch 51/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 47.8121 - mean_absolute_error: 5.4931 - val_loss: 56.1045 - val_mean_absolute_error: 5.9684\n",
            "Epoch 52/100\n",
            "17/17 [==============================] - 0s 10ms/step - loss: 41.7188 - mean_absolute_error: 5.0982 - val_loss: 51.3356 - val_mean_absolute_error: 5.5262\n",
            "Epoch 53/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 44.4086 - mean_absolute_error: 5.2205 - val_loss: 93.1993 - val_mean_absolute_error: 7.9876\n",
            "Epoch 54/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 41.5757 - mean_absolute_error: 5.0661 - val_loss: 77.5702 - val_mean_absolute_error: 7.0950\n",
            "Epoch 55/100\n",
            "17/17 [==============================] - 0s 11ms/step - loss: 42.5944 - mean_absolute_error: 5.0833 - val_loss: 52.9802 - val_mean_absolute_error: 5.5841\n",
            "Epoch 56/100\n",
            "17/17 [==============================] - 0s 13ms/step - loss: 56.1408 - mean_absolute_error: 5.9049 - val_loss: 57.3826 - val_mean_absolute_error: 5.7995\n",
            "Epoch 57/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 61.5166 - mean_absolute_error: 6.3012 - val_loss: 83.1054 - val_mean_absolute_error: 7.3125\n",
            "Epoch 58/100\n",
            "17/17 [==============================] - 0s 13ms/step - loss: 52.6527 - mean_absolute_error: 5.8015 - val_loss: 127.2395 - val_mean_absolute_error: 9.7272\n",
            "Epoch 59/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 48.5566 - mean_absolute_error: 5.4173 - val_loss: 70.5446 - val_mean_absolute_error: 6.7243\n",
            "Epoch 60/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 42.5297 - mean_absolute_error: 5.1187 - val_loss: 134.1243 - val_mean_absolute_error: 10.1313\n",
            "Epoch 61/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 43.9069 - mean_absolute_error: 5.2346 - val_loss: 86.6011 - val_mean_absolute_error: 7.6806\n",
            "Epoch 62/100\n",
            "17/17 [==============================] - 0s 12ms/step - loss: 46.3919 - mean_absolute_error: 5.4362 - val_loss: 48.6101 - val_mean_absolute_error: 5.3779\n",
            "Epoch 63/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.5170 - mean_absolute_error: 5.0195 - val_loss: 53.7028 - val_mean_absolute_error: 5.5723\n",
            "Epoch 64/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 39.5868 - mean_absolute_error: 4.9974 - val_loss: 51.5019 - val_mean_absolute_error: 5.4975\n",
            "Epoch 65/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 38.9038 - mean_absolute_error: 4.8981 - val_loss: 49.3647 - val_mean_absolute_error: 5.2669\n",
            "Epoch 66/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 40.5762 - mean_absolute_error: 4.9834 - val_loss: 52.9264 - val_mean_absolute_error: 5.5189\n",
            "Epoch 67/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 38.9342 - mean_absolute_error: 4.8897 - val_loss: 89.9675 - val_mean_absolute_error: 7.7832\n",
            "Epoch 68/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 39.0830 - mean_absolute_error: 4.8754 - val_loss: 57.3156 - val_mean_absolute_error: 5.8401\n",
            "Epoch 69/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 38.5714 - mean_absolute_error: 4.8259 - val_loss: 57.9906 - val_mean_absolute_error: 5.9128\n",
            "Epoch 70/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 40.2003 - mean_absolute_error: 4.9913 - val_loss: 52.5058 - val_mean_absolute_error: 5.6082\n",
            "Epoch 71/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 43.4299 - mean_absolute_error: 5.1910 - val_loss: 47.5199 - val_mean_absolute_error: 5.2606\n",
            "Epoch 72/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 38.6762 - mean_absolute_error: 4.8415 - val_loss: 60.2634 - val_mean_absolute_error: 6.0138\n",
            "Epoch 73/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 40.0595 - mean_absolute_error: 4.9226 - val_loss: 49.1708 - val_mean_absolute_error: 5.2740\n",
            "Epoch 74/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 39.5649 - mean_absolute_error: 4.8645 - val_loss: 51.3797 - val_mean_absolute_error: 5.4519\n",
            "Epoch 75/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.7661 - mean_absolute_error: 4.9393 - val_loss: 59.0538 - val_mean_absolute_error: 5.9237\n",
            "Epoch 76/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.3311 - mean_absolute_error: 4.9242 - val_loss: 75.5805 - val_mean_absolute_error: 7.0259\n",
            "Epoch 77/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 44.5587 - mean_absolute_error: 5.4023 - val_loss: 101.7311 - val_mean_absolute_error: 8.5299\n",
            "Epoch 78/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 41.2811 - mean_absolute_error: 5.0072 - val_loss: 73.2206 - val_mean_absolute_error: 6.8831\n",
            "Epoch 79/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 37.8487 - mean_absolute_error: 4.8803 - val_loss: 59.2027 - val_mean_absolute_error: 6.0344\n",
            "Epoch 80/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 39.2016 - mean_absolute_error: 4.9416 - val_loss: 46.7422 - val_mean_absolute_error: 5.1251\n",
            "Epoch 81/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 36.9548 - mean_absolute_error: 4.7291 - val_loss: 45.7899 - val_mean_absolute_error: 5.0791\n",
            "Epoch 82/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 39.4199 - mean_absolute_error: 4.9266 - val_loss: 59.0483 - val_mean_absolute_error: 5.9658\n",
            "Epoch 83/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.2423 - mean_absolute_error: 4.9361 - val_loss: 67.8430 - val_mean_absolute_error: 6.5206\n",
            "Epoch 84/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 43.2589 - mean_absolute_error: 5.2126 - val_loss: 55.5336 - val_mean_absolute_error: 5.7037\n",
            "Epoch 85/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 44.2317 - mean_absolute_error: 5.2489 - val_loss: 110.8242 - val_mean_absolute_error: 8.9130\n",
            "Epoch 86/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 49.0135 - mean_absolute_error: 5.5595 - val_loss: 109.3444 - val_mean_absolute_error: 8.9072\n",
            "Epoch 87/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 41.3656 - mean_absolute_error: 5.0542 - val_loss: 63.4378 - val_mean_absolute_error: 6.2626\n",
            "Epoch 88/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 50.8891 - mean_absolute_error: 5.6171 - val_loss: 61.3346 - val_mean_absolute_error: 6.1381\n",
            "Epoch 89/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 48.1056 - mean_absolute_error: 5.4335 - val_loss: 45.6129 - val_mean_absolute_error: 5.2596\n",
            "Epoch 90/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.2004 - mean_absolute_error: 4.9414 - val_loss: 45.9045 - val_mean_absolute_error: 5.0775\n",
            "Epoch 91/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 37.3380 - mean_absolute_error: 4.8129 - val_loss: 44.4584 - val_mean_absolute_error: 4.9633\n",
            "Epoch 92/100\n",
            "17/17 [==============================] - 0s 9ms/step - loss: 36.7507 - mean_absolute_error: 4.6888 - val_loss: 92.5038 - val_mean_absolute_error: 8.0394\n",
            "Epoch 93/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 51.7147 - mean_absolute_error: 5.6733 - val_loss: 112.5611 - val_mean_absolute_error: 9.0486\n",
            "Epoch 94/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 41.9490 - mean_absolute_error: 5.1094 - val_loss: 46.3748 - val_mean_absolute_error: 5.0498\n",
            "Epoch 95/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 41.3561 - mean_absolute_error: 5.0001 - val_loss: 58.7404 - val_mean_absolute_error: 5.9421\n",
            "Epoch 96/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 48.7455 - mean_absolute_error: 5.4648 - val_loss: 109.9467 - val_mean_absolute_error: 8.9095\n",
            "Epoch 97/100\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 49.6344 - mean_absolute_error: 5.6472 - val_loss: 119.6084 - val_mean_absolute_error: 9.2698\n",
            "Epoch 98/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 53.8615 - mean_absolute_error: 5.7834 - val_loss: 55.4883 - val_mean_absolute_error: 5.7359\n",
            "Epoch 99/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 40.0334 - mean_absolute_error: 4.9393 - val_loss: 44.9171 - val_mean_absolute_error: 5.2256\n",
            "Epoch 100/100\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 44.6041 - mean_absolute_error: 5.2464 - val_loss: 41.8353 - val_mean_absolute_error: 4.9722\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7dbf7a0fa170>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predictions = model.predict(X_train).flatten()\n",
        "\n",
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.legend(['Training Predictions', 'Training Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "34N0MxkXo32n",
        "outputId": "a3a4178e-81c5-41c9-c5c6-1622375c0c6b"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/17 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf81064700>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_predictions = model.predict(X_val).flatten()\n",
        "\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.legend(['Validation Predictions', 'Validation Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "6ufahe8Bo8Eh",
        "outputId": "c8159357-6a65-4e15-ff95-8b895144fe9f"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 5ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf82d2f490>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(X_test).flatten()\n",
        "\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.legend(['Testing Predictions', 'Testing Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "8a3G7N_Do-hF",
        "outputId": "cd262189-66cd-4f50-8948-b3f81e191d66"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf82d168f0>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.legend(['Training Predictions',\n",
        "            'Training Observations',\n",
        "            'Validation Predictions',\n",
        "            'Validation Observations',\n",
        "            'Testing Predictions',\n",
        "            'Testing Observations'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "RP244hA1pFuf",
        "outputId": "830823f0-4380-486b-a338-91ea3006d621"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf82ba0bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "from copy import deepcopy\n",
        "\n",
        "recursive_predictions = []\n",
        "recursive_dates = np.concatenate([dates_val, dates_test])\n",
        "\n",
        "for target_date in recursive_dates:\n",
        "  last_window = deepcopy(X_train[-1])\n",
        "  next_prediction = model.predict(np.array([last_window])).flatten()\n",
        "  recursive_predictions.append(next_prediction)\n",
        "  last_window[-1] = next_prediction"
      ],
      "metadata": {
        "id": "dXSnGhuTpBDA"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dates_train, train_predictions)\n",
        "plt.plot(dates_train, y_train)\n",
        "plt.plot(dates_val, val_predictions)\n",
        "plt.plot(dates_val, y_val)\n",
        "plt.plot(dates_test, test_predictions)\n",
        "plt.plot(dates_test, y_test)\n",
        "plt.plot(recursive_dates, recursive_predictions)\n",
        "plt.legend(['Training Predictions',\n",
        "            'Training Observations',\n",
        "            'Validation Predictions',\n",
        "            'Validation Observations',\n",
        "            'Testing Predictions',\n",
        "            'Testing Observations',\n",
        "            'Recursive Predictions'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KgvRQ_65pDg2",
        "outputId": "78de2ebc-43fc-4eef-a67f-9d665b2257dc"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dbf81064a90>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}