darts.ipynb

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            "\u001b[K     |████████████████████████████████| 62 kB 309 kB/s \n",
            "\u001b[K     |████████████████████████████████| 636 kB 7.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 424 kB 9.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 705 kB 40.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.8 MB 50.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 76.6 MB 73 kB/s \n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 39.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 44 kB 2.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 43 kB 726 kB/s \n",
            "\u001b[K     |████████████████████████████████| 11.2 MB 39.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 53.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 957 kB 55.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.9 MB 47.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 419 kB 52.0 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.2+zzzcolab20220719082949 requires tensorboard<2.9,>=2.8, but you have tensorboard 2.10.0 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 10.2 MB/s \n",
            "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install yfinance -q\n",
        "!pip install pyyaml==5.4.1 -q\n",
        "!pip install darts -q\n",
        "!pip install -U pip -q\n",
        "!pip install -U matplotlib -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib==3.5.3 -q"
      ],
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        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from darts import TimeSeries\n",
        "from darts.metrics import mape\n",
        "import yfinance as yf\n",
        "from darts.models import AutoARIMA, KalmanForecaster, LinearRegressionModel, NBEATSModel, ExponentialSmoothing, Theta, TCNModel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVjtebOeZHxD",
        "outputId": "b0d8ae08-3abc-4056-8b57-b17706c801eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = pd.DataFrame(yf.Ticker('^GSPC').history(period=\"max\"))\n",
        "sp500['date'] = sp500.index\n",
        "sp500"
      ],
      "metadata": {
        "colab": {
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          "height": 455
        },
        "id": "xqvSuk4IdTjj",
        "outputId": "b3306a84-30dd-4126-bd45-587ecb28b581"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Open         High          Low        Close      Volume  \\\n",
              "Date                                                                         \n",
              "1950-01-03    16.660000    16.660000    16.660000    16.660000     1260000   \n",
              "1950-01-04    16.850000    16.850000    16.850000    16.850000     1890000   \n",
              "1950-01-05    16.930000    16.930000    16.930000    16.930000     2550000   \n",
              "1950-01-06    16.980000    16.980000    16.980000    16.980000     2010000   \n",
              "1950-01-09    17.080000    17.080000    17.080000    17.080000     2520000   \n",
              "...                 ...          ...          ...          ...         ...   \n",
              "2022-08-23  4133.089844  4159.770020  4124.029785  4128.729980  3117800000   \n",
              "2022-08-24  4126.549805  4156.560059  4119.970215  4140.770020  3056910000   \n",
              "2022-08-25  4153.259766  4200.540039  4147.589844  4199.120117  2976050000   \n",
              "2022-08-26  4198.740234  4203.040039  4057.659912  4057.659912  3175260000   \n",
              "2022-08-29  4034.580078  4062.989990  4017.419922  4030.610107  2963020000   \n",
              "\n",
              "            Dividends  Stock Splits       date  \n",
              "Date                                            \n",
              "1950-01-03          0             0 1950-01-03  \n",
              "1950-01-04          0             0 1950-01-04  \n",
              "1950-01-05          0             0 1950-01-05  \n",
              "1950-01-06          0             0 1950-01-06  \n",
              "1950-01-09          0             0 1950-01-09  \n",
              "...               ...           ...        ...  \n",
              "2022-08-23          0             0 2022-08-23  \n",
              "2022-08-24          0             0 2022-08-24  \n",
              "2022-08-25          0             0 2022-08-25  \n",
              "2022-08-26          0             0 2022-08-26  \n",
              "2022-08-29          0             0 2022-08-29  \n",
              "\n",
              "[18283 rows x 8 columns]"
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              "      <th>Open</th>\n",
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              "      <th>1950-01-03</th>\n",
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              "      <td>16.930000</td>\n",
              "      <td>16.930000</td>\n",
              "      <td>16.930000</td>\n",
              "      <td>16.930000</td>\n",
              "      <td>2550000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1950-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950-01-06</th>\n",
              "      <td>16.980000</td>\n",
              "      <td>16.980000</td>\n",
              "      <td>16.980000</td>\n",
              "      <td>16.980000</td>\n",
              "      <td>2010000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1950-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950-01-09</th>\n",
              "      <td>17.080000</td>\n",
              "      <td>17.080000</td>\n",
              "      <td>17.080000</td>\n",
              "      <td>17.080000</td>\n",
              "      <td>2520000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1950-01-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-23</th>\n",
              "      <td>4133.089844</td>\n",
              "      <td>4159.770020</td>\n",
              "      <td>4124.029785</td>\n",
              "      <td>4128.729980</td>\n",
              "      <td>3117800000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-08-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-24</th>\n",
              "      <td>4126.549805</td>\n",
              "      <td>4156.560059</td>\n",
              "      <td>4119.970215</td>\n",
              "      <td>4140.770020</td>\n",
              "      <td>3056910000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-08-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-25</th>\n",
              "      <td>4153.259766</td>\n",
              "      <td>4200.540039</td>\n",
              "      <td>4147.589844</td>\n",
              "      <td>4199.120117</td>\n",
              "      <td>2976050000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-08-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-26</th>\n",
              "      <td>4198.740234</td>\n",
              "      <td>4203.040039</td>\n",
              "      <td>4057.659912</td>\n",
              "      <td>4057.659912</td>\n",
              "      <td>3175260000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-08-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-08-29</th>\n",
              "      <td>4034.580078</td>\n",
              "      <td>4062.989990</td>\n",
              "      <td>4017.419922</td>\n",
              "      <td>4030.610107</td>\n",
              "      <td>2963020000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-08-29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18283 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f1f0a38-9f30-41f9-beba-41c619c5852e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f1f0a38-9f30-41f9-beba-41c619c5852e 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-3f1f0a38-9f30-41f9-beba-41c619c5852e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.set_index('date').asfreq('d', method='bfill')\n",
        "print(sp500.index.freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WomMKe0EdqCd",
        "outputId": "0df797c0-07f9-4093-f72d-cb03a75d51ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Day>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500Month = sp500.Close\n",
        "sp500Month.index = pd.to_datetime(sp500.index)\n",
        "sp500Month = sp500Month.resample('1M').mean()\n",
        "len(sp500Month)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WfvU_EIeA59",
        "outputId": "1a7f5ddc-8b68-49f8-c7e1-de95086e6314"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "872"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prices = TimeSeries.from_series(sp500.Close) \n",
        "prices_month = TimeSeries.from_series(sp500Month) "
      ],
      "metadata": {
        "id": "HgSh2wurdlMR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, val = prices.split_before(0.9)\n",
        "train_m, val_m = prices_month.split_before(0.9)"
      ],
      "metadata": {
        "id": "eykVgSw3dc2V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_m)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNeLsyNKe3GY",
        "outputId": "c9937bc5-d405-4503-88ca-8b3656437988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "783"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **NBEATS MODEL - facebook**"
      ],
      "metadata": {
        "id": "ky2DQzGBZ3Tn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = NBEATSModel(input_chunk_length=24, output_chunk_length=12).fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794,
          "referenced_widgets": [
            "d467f50a649649c8b858ab723d03ccea",
            "305d02b79f654a989e0af4180ca608e8",
            "b5daa2b39c4342369924cde2a52ccc4b",
            "967a9feacba94187b6fb9613b48a2ed6",
            "0007713da8bf4a7ba5e3f33ca5e68167",
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            "b1ebe0431dff48d9844ecd9cceae278b",
            "c784f0eef50745ceb7155f593cf2733c",
            "e14510b98e5f4a80a1a2bf3d5456a47b",
            "ce6b1e2639304a2cbf2926e4cc0d786b",
            "f5d3457b43184a0ba4c306c03f43b54f",
            "f75547fe7a04406ea5d3fd453a5675b2",
            "cfb50862b37c4771a2b3ec6f16b9c960",
            "05c9f8980de54bfc9dded55c884ce47c",
            "49d8573e1cf14cbaa8a6cc2c9dc501bb",
            "62296069d8d742cc9059816c13151d86",
            "40818b724d614fd5b5103c0b9d0e460b",
            "195d6ca6d2714926a1116511185867dc"
          ]
        },
        "id": "g4jSgLdheTSl",
        "outputId": "b537d536-8dd1-44a5-8e30-07d28cd7f1aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name          | Type             | Params\n",
            "---------------------------------------------------\n",
            "0 | criterion     | MSELoss          | 0     \n",
            "1 | train_metrics | MetricCollection | 0     \n",
            "2 | val_metrics   | MetricCollection | 0     \n",
            "3 | stacks        | ModuleList       | 6.2 M \n",
            "---------------------------------------------------\n",
            "6.2 M     Trainable params\n",
            "1.4 K     Non-trainable params\n",
            "6.2 M     Total params\n",
            "49.575    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d467f50a649649c8b858ab723d03ccea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:653: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
            "  rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 17it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c784f0eef50745ceb7155f593cf2733c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2092\u001b[0m         \u001b[0mratio\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mratio\u001b[0m \u001b[0mof\u001b[0m \u001b[0mlogical\u001b[0m \u001b[0mto\u001b[0m \u001b[0mphysical\u001b[0m \u001b[0mpixels\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2094\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2095\u001b[0m         \u001b[0mReturns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[0;34m(figure, print_method)\u001b[0m\n\u001b[1;32m   1558\u001b[0m     \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_non_interactive_terminal_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1562\u001b[0m     \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mMetadata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0mpairs\u001b[0m \u001b[0mof\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlatin\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0mencodable\u001b[0m \u001b[0mstrings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             \u001b[0mAccording\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mspecification\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0mmust\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mshorter\u001b[0m \u001b[0mthan\u001b[0m \u001b[0;36m79\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m             \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name '_png' from 'matplotlib' (/usr/local/lib/python3.7/dist-packages/matplotlib/__init__.py)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Theta model**"
      ],
      "metadata": {
        "id": "Rr1zlGZEZ8U3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Theta().fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "Yt5mpWhVimGw",
        "outputId": "a24cc202-0882-4d7b-eb90-ac76ac9a8ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2092\u001b[0m         \u001b[0mratio\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mratio\u001b[0m \u001b[0mof\u001b[0m \u001b[0mlogical\u001b[0m \u001b[0mto\u001b[0m \u001b[0mphysical\u001b[0m \u001b[0mpixels\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2094\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2095\u001b[0m         \u001b[0mReturns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[0;34m(figure, print_method)\u001b[0m\n\u001b[1;32m   1558\u001b[0m     \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_non_interactive_terminal_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1562\u001b[0m     \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mMetadata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0mpairs\u001b[0m \u001b[0mof\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlatin\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0mencodable\u001b[0m \u001b[0mstrings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             \u001b[0mAccording\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mspecification\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0mmust\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mshorter\u001b[0m \u001b[0mthan\u001b[0m \u001b[0;36m79\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m             \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name '_png' from 'matplotlib' (/usr/local/lib/python3.7/dist-packages/matplotlib/__init__.py)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Eksponencijalno izravnanje**"
      ],
      "metadata": {
        "id": "HpkhGRg0aB5W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ExponentialSmoothing().fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "VONYm_HrirOU",
        "outputId": "f44c7ff3-7112-4273-9d82-6b3e7656b772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AutoARIMA**"
      ],
      "metadata": {
        "id": "mVc0owxNaHXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoARIMA().fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "ZAfu_tVoi80s",
        "outputId": "b95008be-d348-4023-f284-99969f887518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Kalmanova Prognoza**"
      ],
      "metadata": {
        "id": "6Vu6upiTaKhu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = KalmanForecaster().fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "kxCw5NiJjWzH",
        "outputId": "d43b158e-e163-4b3e-8ec3-288a4ba67350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Linearna Regresija**"
      ],
      "metadata": {
        "id": "JHlXD2rUaPNX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegressionModel(lags=12).fit(train_m)\n",
        "forecast = model.predict(len(val_m))\n",
        "\n",
        "train_m.plot()\n",
        "val_m.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "b0AwO1pIkUqJ",
        "outputId": "c8a2acde-a827-49fb-ef74-272614030f7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from darts.dataprocessing.transformers import Scaler\n",
        "scaler_sp500 = Scaler()\n",
        "\n",
        "scaler_sp500_scaled = scaler_sp500.fit_transform(prices_month)"
      ],
      "metadata": {
        "id": "wJcnSWpZjs1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, val = scaler_sp500_scaled.split_before(0.9)"
      ],
      "metadata": {
        "id": "aTI56LJ4km_4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegressionModel(lags=12).fit(train)\n",
        "forecast = model.predict(len(val))\n",
        "\n",
        "print(\n",
        "    \"Mean absolute percentage error for the combined naive drift + seasonal: {:.2f}%.\".format(\n",
        "        mape(val, forecast)\n",
        "    )\n",
        ")\n",
        "\n",
        "train.plot()\n",
        "val.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "5M9gY0v4knVl",
        "outputId": "c08bde87-cb9b-4150-b753-c5accc7438b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error for the combined naive drift + seasonal: 15.63%.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoARIMA().fit(train)\n",
        "forecast = model.predict(len(val))\n",
        "\n",
        "print(\n",
        "    \"Mean absolute percentage error for the combined naive drift + seasonal: {:.2f}%.\".format(\n",
        "        mape(val, forecast)\n",
        "    )\n",
        ")\n",
        "train.plot()\n",
        "val.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "e-2uVuVckuwo",
        "outputId": "fee6fbb1-3b06-4751-aaae-0e627722eb70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error for the combined naive drift + seasonal: 11.96%.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BDxK6MO6q9iQleWa1e0e6U9YC11u3bwTWVKpbA/SxbvcB9jkrMCGE8JQA/7rruibBtqd2YD5xjp2h0XwSn0K0BzuSL5jEtdaZwHGl1GogHfiPUuof1rqfgCyl1NfAGOBvrgtVCCHco6S07rqoH49x5JOjmEL8eD0pHbPJj2gPra8JDg4x1FpPqlY0rlLd750akRBCeFj1WQrbxcORUxBXVgIztgMQ93h3jnxuZG9PLZIMcrGPEELUkJtf9fTdFWmAxcJjR7ZjKSgnYVA8sSOT7fWebIlLEhdCiGqO5Va936E1DDl9mMsKc/CPCaTnmz0orzh/IjM4yDMnNUGSuBBC1GBbCDm1A3z4BxMJxWcZfdy4dLPT8xcT0sY9k1s5wqE+cSGEaEkOnzRu50010aeLhU8nbyPUXMG3Ua25bngbAFJT4Nc3FdE33bPXOEoSF0KISsrKLew5bGx3agf7/naAsH15nAoIZmbbi3nF2nViMpn44335JCVFejBa6U4RQogqtvwMxefgovbgt6+APa/sBeAvSWncdL17J7dyhLTEhRCiElsrvFdyBZsf+glLuYWUBzqw4NEE2rtvmnCHSRIXQohK9ltnihq4bS+Fu4oI7xpO6h8uwj/McyNQ6iNJXAghKtl92EJa0Wk6bD+Eyd9E77/3xD+snuvwPUySuBBCWJ04beHDL8qZkb0VkwU6P9qJmEujPR1WveTEphBCWL32oYXfHNtFm7ISIntG0u3JLp4O6YIkiQshhNWRL05wY94RCPSjz6xe+AV5f4r0/giFEMINcg6WMFgbk1t1/n03IlN9Y6EySeJCiBbPYrHw/q3biakoY09cK7pPSPF0SA6TJC6EaPGyFxyh2+GTFPoFcPCXaZj8vHM4YW1kdIoQokUrPlzM1id3AjCv40X897kwD0fUMNISF0K0WBazhZ8e3oq5sJy1kQkc7tUOk8l3WuEgSVwI0YLtn3WQnO9yMUcFMaNdGiltfCuBgyRxIUQLVbCjgN0vGHOEf3NNGmcCgsjoIUlcCCG8nrnUzOaHtmA+Z6b9qCRW+CcA0L+3hwNrBEniQogWZ+9rP5O/pYDQlFDin+jOriyjPDnBs3E1hiRxIUSLcvqHPPa+sQ9MkPJKOl3H+HO2BPz8oHWsp6NrOEniQogWo7yonM0P/QRm6DyhI+v9WnG2xKgzmyEgQPrEhRDCK1ksFpaN28XZ/cVEpkfQ7elubN5rsdc/e7/nYmsKSeJCiBZhxpMH4cvDlPuZuPgvPfEP9rMvADH5Hphyr++1wkGu2BRCtAAL/7eYyHdDAfh3QldueyyCYVeb+fRbo37IlSYCfbArBSSJCyGauRMnTnDLL4aQEnIlNye/zWdxxuRWtgQO0Mv7pw2vk3SnCCGatblz5wJwsGQdZ4edwFLtsvo/TzARFe6brXCQJC6EaOZWr15t375pQHqN+mFXuzMa55MkLoRotv75z3+ycOFCAObMmUP6RfE19kmIcXNQTiZJXAjRLJWXl/PAAw/Y78fHx5MYU3O/0GD3xeQKDp3YVEpNBzKAA8AYrXVZtfrJwHCttXJ6hEII0Qha6yr3S0tLia9l4Xpfm3q2ugu2xJVSvYEkrXV/YCcwvFp9JNDTNeEJIUTj7Nixw75tMpkYMGAAAQEm3pjg20m7Oke6UzKAZdbtJcBV1eofA2Y4MyghhGiqXbt2ATBlyhROnz5NYmIiABNHmJg31UjkV/vgrIXVOdKdEgtYr2viDNDKVqGUigZ6aq1fUKr2nhSl1FhgLMCECRMYNGhQowItKysjOzu7UY91J4nTuSRO52opcRYUFDB9+nQAOnXqRGFhIYWFhfb6a9Lgo2eC6NS2nOxss8fidFRSUlKddY4k8TwgyrodDeRWqpsI/K2+B2utZwOzrXct9e1bn+zs7Hp/EW8hcTqXxOlcLSXOefPm2beHDx9OVFRUjX1GOOHP4A1/T0e6U9YC11u3bwTWVKrrCkxRSi0BuimlnnFyfEII0SBnz57lV7/6FQAPPPBArQm8OblgEtdaZwLHlVKrgXTgP0qpf1jrRmmtb9Ja3wTs0Vq/6NJohRDiAj744AP79l133eXBSNzDoSGGWutJ1YrG1bKPDC8UQnjc8uXLAZg8eTLXXXedh6NxPbnYRwjRrPz8888A3HrrrR6OxD0kiQshmpX9+/cDxqiUlkCSuBCi2Th69Ci5ublERkbSunVrT4fjFpLEhRDNxrRp0wC44oorfP5yekdJEhdCNAubNm1i1qxZAIwaNcrD0biPJHEhRLNw2WWX2bcliQshhI+69NJLPR2CW0kSF0L4vIqKCvv2//73Pw9G4n6SxIUQPu/AgQMAtGnThuTkZM8G42aSxIUQLmexWFi6dCnx8fE88sgjTn/+VatWAcaolJZGkrgQwuVGjx7NTTfdRE5ODjNmOH/5Adul9o2d6tqXSRIXooU5deoUAwYM4E9/+pNbjnfu3Dn+/e9/Vyk7ceKE057fbDazYsUKQJK4EKKZy8zMJCEhgW+++YYnn3ySgoIClx9z9+7dNcrGjh3rtOfftWsXOTk5tG/fnq5duzrteX2FJHEhWgitNUOHDq1StmfPHpces6Kigl69etUor7z+ZVOsWLGCtLQ0ANLT01vMVZqVSRIXogUoLCykb9++NcoPHz7s0uO+88479u2kpCRuueUWAJKTk9m3b1+Tn//NN9+0b3fr1q3Jz+eLJIkL0czl5OTw9ttv11qXlZXl0mP/9a9/tW8//PDDPPXUUwCsXLmSLl26sGXLlkY976pVqxgxYgQLFy60l6WnpzctWB8lSVyIZu6aa67h8ccft99PTU3ljTfeAM6P6nCFn3/+2Z6kX3/9dSZNmmRfcd6msRfmPP/883z88cf2+2lpaS1iFZ/aSBIXohk7cuQI27Zts9/v2LEjK1as4I477gCMJFpeXu6SY3/11VcAjBgxgt/+9rcEBATUmOO7tLTUoeeqqKjg7bff5vvvvwfOjwsH+L//+z+2bdtGdHS0kyL3LQ4tzyaE8E033HBDlfu/+c1vaNeuHRaLhdjYWE6fPs3UqVN5+eWXnX7szZs3A9CvXz97WUBAABERERQWFgJw8ODBCz5PSUkJoaGh9vuff/55lfo2bdo4I1yfJS1xIZqp4uLiKq1wwJ4MTSYT11xzDWD0TzvTxx9/TK9evfj73/8OQJ8+farUT5p0fsneo0ePXvD5lixZUuV+9WXXHn744UZG2jxIEheimbIl8G7duvGnP/2J1NRUBg4caK+fOXMmABs2bGDBggVOOeYXX3zBiBEj7H3h4eHhXHXVVVX2mTJlCi+88AJg9Mnb1sSsS2ZmZq3ld999t/3CpZZMkrgQzdTUqVMB6Nu3L0888QQ7duwgPj7eXh8bG2vfHjlyJMXFxU06nsVi4dlnn61S1rt3b0JCQqqU+fn58cADD9jv//Of/6z3eTdu3AhQ43nS09OJi4trSsjNgiRxIZopW/K75JJLaq2v3M8MsHr16kYf69ChQ6SmppKZmUl0dDR33XUX4eHhvPrqq7Xun5CQYN+2WCz1Prft96g8Jhyo9SKilkiSuBDNUEFBASdPngTg0UcfdegxS5cubfBxZsyYQWpqKhdffLH98vqEhAQ++OADjh07VqMrxcbPz89+IdChQ4dq1O/Zs4c333yTw4cPc/ToUaKioqos9hAfH8/111/f4HibIxmdIkQzZLucPi0tjaCgoDr3M5lM9pbw0qVLef311x0+xv79+2udVnbo0KGYTCYiIiLqfXyrVq0AyM/Pr1E3cOBALBYLX375JWAsvRYTE2Ovv/fee2t0r7RU0hIXopkZN26cfb3Jnj171rvvlClTaNeuHWCcCF2wYAGPPvporYkVoKysjG+//Raz2WwfB26Tnp7Om2++yUsvveRQnLYkXFtfvO2DZdmyZYAxVDI5Odn+gVR9DpiWTFriQjQj+fn5zJ49236/+vC+6qZNm8a0adOIjIyksLCQkSNHApCbm8v7779fY/9x48ZVmQ8FICMjg5iYGGbNmkX79u0djtXWJ19SUlKlfOfOnTX27du3L6GhoWzevJnNmze3+BEplUkSF6KZsFgsNZKbrZV9IYGBgVXur1mzpsY+ZrO5RgIHY9z3L37xC4fjtLEl8cot8bKyMi6++OIa+9rmRUlNTSU1NbXBx2rOpDtFiGZiz549/Pjjj1XKHB2Cd/r06Sr3c3Nza+xT2wlIaPxCDLUl8cWLF9fYr3379i3+qsz6SBIXoplYv359jTLbycOGys/P58iRI1XKbH3gV199tX2yqffee4/w8PBGHaO2JD537twa+0n/d/0kiQvRTOzatatGmaNJvHPnzgBMnDiRyy+/HKDGlZRz5swB4Ne//jXz58/HbDbzq1/9qtHxVk/iubm5LFy4EH9/fzZt2sSWLVuYMWMGr732WqOP0RJIEheimdi7dy8AgwcPtpe1bdvWocd+8cUXTJ8+nVdffZWOHTsCcODAAXv9li1b2LBhA9HR0QwfPhygyavoVE/ia9eupby8nIyMDBITE+nRowcPP/wwYWFhTTpOc+fQiU2l1HQgAzgAjNFal1nLbwGmAGXARq31Yy6KUwhxAbax4Y899hgmk4lhw4YRFRXl0GPT0tLsy5ylpKQA5xeMKCoqsl8dOWLECKclVdsQQ9voFFt/vu2bgHDMBVviSqneQJLWuj+wExheqXozcJXW+v+ARKWUck2YQoj6nD17lk2bNgFw6aWXsnDhQsaMGdOo57LNr5KTkwPAhx9+aK8bNWpUEyM9Lzg4GJPJRGlpKRUVFfZpabt06eK0Y7QEjnSnZADLrNtLAPt1tFrrQ1pr24zypYDZueEJIS7k6NGj9pOLbdu2bfKkULYkfurUKeD8vOATJ06kf//+TXruykwmk/2bQl5enr3ln5yc7LRjtASOdKfEArZJf88ANc6UKKX6Aola60211I0FxgJMmDCh0cORysrKyM7ObtRj3UnidC6Js34lJSVVRm+8/PLLNUaVVOZInLa+7nfffZfhw4ejtQaMFr6zf8e4uDjOnDnDypUr+eabbwCjr1z+71UlJSXVWedIEs8DbB1r0UCVAaRKqWTgTeD22h6stZ4N2C4hq3+6snpkZ2fX+4t4C4nTuSTO+q1cudJ+hePSpUtrrORTnSNxdu/e3b5deQGGAQMGOP13tK16P2fOHM6dO8eAAQMYOHAgR44ckf+7gxzpTlkL2KYLuxGwX8qllIoEPgTGaa1POD88IUR9bAsmPPjggxdM4I6qPFugTVRUlEu6OWwX8dgWbL7//vubPOqlpblgEtdaZwLHlVKrgXTgP0qpf1irJwKdgBlKqa+VUte4KlAhRE22/urevXs77TmDg4P57W9/W6UsPj7eJcn13nvvrXK/W7duTj9Gc+fQEEOt9aRqReOs5c8Dzzs7KCGEY1yRxMGYD2XZsmVs3boVwKknNCsbMmQIPXr0sB+nU6dOLjlOcyYTYAnhoz788EN7Eq/cj+0Mbdq0YcuWLaxdu5alS5fyu9/9zqnPX1lycrI9iVde8Uc4RpK4ED6ovLycX/7yl/b7ldfLdKaMjAwyMjJc8tw2lS9ICgiQlNRQctm9ED7IdmGPjS+fDJQZCptGkrgQPuipp56yb7/11lsejKTpxo8fD9CkybRaMvnuIoSPOXToEF9//TVBQUHs27fP4+OUm6p79+6cPHnSZV1CzZ0kcSF8iMVi4b///S8AN998s88ncBvbpf6i4SSJC+Ej9u3bxy233ML27duBxq+oI5oXSeJC+ICcnBy6du1qXwUe4O677/ZgRMJbyIlNIXzAY489ViWBv/XWW9KHLABJ4kJ4vYMHD/Lpp58CMH36dCwWCw899JCHoxLeQpK4EF6stLSUq666iuLiYvr168ekSdVnwBAtnSRxIbzYhAkTyM7OJjY2ls8//9ynL+oRriFJXAgv9fPPP/Ovf/0LgM8++4zWrVt7OCLhjSSJC+Glnn76aSoqKrj//vu55hqZ5VnUToYYCuFhmzZtYsmSJWRmZvLoo4/aZw/8+OOPCQsL47nnnvN0iMKLSRIXwgNWrFjB888/z759++wLBAN8/PHHVfZ76qmnSElJcXd4wodIEhfCDdatW0dFRQXbtm3jjTfeYNeuXfY6Pz8/hgwZwqJFizCbzQB07dqVLl261FhhR4jqJIkL4UJnz57lo48+YsyYMTXqYmJiePnllxk0aBBdunQhNzeXgwcPkpaWRnBwsAeiFb5IkrgQTmSxWDCZTGzatIknnniC77//nuLi4ir7DBw4kBtuuKHKdLIArVq1olWrVu4MVzQDksSFaKSKigree+89li9fTr9+/Vi4cCFr165l3rx5jBs3jhMnTgBGd0lGRgYfffQR7dq183DUormRJC5EHZYuXcqiRYt48cUXiYyMrFE/ffp0nnnmGQA++OADe/ntt98OQGJiIkuXLqV3795ykY5wGUniQtTiwIEDDB06lPLycvz9/XnjjTfsdRaLhVmzZjF16tR6n2PYsGH06dPHxZGKlk4u9hGimrfffptOnTpRXl4OwHfffQdAVlYWy5cvJykpifHjx2M2m3n22WexWCx89913lJSU2Oc2iY2N5YUXXvDY7yBaDmmJC4HRus7NzSU/P7/GJFNa61q7Q37zm9/YL8Tp2LEjwcHBvPrqqzz88MN06NBBulCEW0hLXLR4GzZsIC0tjfj4eDp37kxeXh7XXnstZrOZnj171vqYZcuWMXv27FoTdUpKiiRw4TaSxEWLtmXLFq699lp27txpL+vevTtz5szBZDIxfvx4goKCuOeee1iyZAmFhYVYLBZZGk14DelOES2KxWLhq6++Yvny5SxfvpzMzEwA+vbty6JFizh9+jRdunTB398fgAcffJBx48ZJy1p4LUniotnZsGED06ZN44cffiA5OZm2bdsyYMAAvv76azZt2sTRo0er7N+nTx+WLVtGTEwMCQkJNZ5PErjwZpLEhU8wm80sXryY7OxsLBYLbdq0oV+/flUunlm/fj3Tpk1j8eLF9jLbBTeLFi2yl5lMJkaOHElGRgaRkZGMGDGCsLAw9/0yQjiRJPFmzmw2s337dqKjo4mMjCQqKgo/v9pPhZw9e5agoCACAgIoKipi/fr15OTkcPDgQX788UdMJhODBg0iKSmJ0tJSbr755gu2UouLizl+/DgFBQWEhYXRrl07QkNDa+x3/PhxoqOjCQ4OprS0lMzMTM6cOcPixYuJjIzkyy+/ZOPGjTUeFxMTQ2pqKvHx8SxcuBCAkJAQxo8fz+WXXw4YMwaeOHGCY8eO2csvuuiihv4phfBKksS9kNls5uTJk2RlZXH48GEKCgrIzc2loKCAU6dOcfr0aQIDA+nZsycDBw6kuLiYZcuWsXXrVg4fPkxJSQmAfUIl28x4AG3btiUlJYXOnTtz3XXXMWzYML799lvmzp3LF198gZ+fHxEREeTm5tYa27x58+zbERERXHfdddxzzz0MHz6cnJwc5s+fz48//khcXBwLFizg0KFDVR5vMpmIiIggLi6OtLQ0EhMTWblypX2/sLAwzp49W+uxExISuPjii4mMjGTdunXk5uaSl5fH999/b9/nzjvvZObMmVW6RUaMGNHA/4AQvsNksVguuJNSajqQARwAxmity6zl/sAcoBuwUWs98QJPdeGD1SE7O5ukpKTGPtxlLBYLJ0+e5ODBg4SGhlJcXEzHjh0pKiqiVatW7Nmzh7Vr13Lo0CHy8vLIysoiODiYjIwMhgwZwu7du/nhhx/Iysqy/2RnZ1NaWuq0GOPi4igtLaWgoKDe/fz8/Kok/N69e9O+fXsiIiJIT08nKCiI9evXs2HDBg4fPlzlsfHx8Zw6darGcwYEBNCmTRsCAwPJy8sjPz+fioqKC8acnJxMaGgoe/bsoXPnzowaNYpJkyYRHh4OGPOWbNq0iaKiIhYuXEheXh4dOnTgiSee8EjXiLe+PquTOJ3LjXHW+ZX3gi1xpVRvIElr3V8p9QwwHJhvrR4KHNFaj1FKzVFKXam1XueUkF3EYrFQWFjI0aNH7T979+61l+Xk5FBQUIDFYiE2NpaYmBhOnDhBYGAg/fr1o7y8nOPHj3Po0CEOHDjAgQMH6mw51ufzzz9n8uTJddbHxcXRvn17kpKSCAsLo02bNkRGRhIdHU1iYiInT57khx9+4MsvvyQhIYHrr7+e/v37k5ycTEFBAVlZWfTq1Yu0tDRatWqFyWSitLSUjRs3kpeXx6pVq1iwYAEHDx6kZ8+e3HPPPdx7771ERESwa9cu/P396dOnT53dJTk5Oaxbt45Vq1YxY8YMewK/4oor6NOnDyUlJVxxxRWMGjWqSlItKysjPz+frKws9u7dy86dO1FKceWVV1JSUkJpaSnt2rWzjw6p603i7+9P3759ARgwYECD//5CNBeOdKdkAMus20uA0ZxP4hnAokp1VwFOT+K7d+9m/vz5BAcHU1xcTElJCSUlJbVu227PnTuHn58fISEhREREUFpaak/ajUm6AF988UWt5TExMaSkpFBSUkJeXh7l5eUEBgZy+vRpOnToQEZGBl27diU2Npa4uDjy8/OZOXMmhYWFxMXFcfPNN9O5c2eSk5Np3749ycnJLmlNBgUFceWVVwLQq1cvXnnlFYqKimpM7qSUuuBzxcXFMXToUIYOHcr48eP55ptviIuL49Zbb623nzwwMJC4uDji4uJqzCsSHR3d8F9KiBbOkSQeC9jGZJ0BWlWry6+jzmkyMzOdus5gaGgobdu2tf8kJycTHx9P69atiYqKIjIykpCQEPLy8sjNzSUhIYGcnBx27dpFUFAQiYmJJCUl0alTJzp27EhMTIz9uR39ejV27Fin/T6N5efnV+vsfA3VpUsXunTp4oSIhBAN5UgSzwOirNvRQK6DdQAopcYCYwEmTJjQqCvdYmNjGT16NKGhoYSEhBAcHExwcDAhISE17gcHBxMUFERwcDAWi4WSkhKKiooICAigdevWJCYmEhER0aixvzfccEONsqKiIoqKiuz3y8rKyM7ObvBzu5vE6VwSp3NJnFXV1zB0JImvBX4LvAvcCKypVnc98K217p3qD9ZazwZmW+826sRmUlISaWlpcqLDiSRO55I4nUvidNwF507RWmcCx5VSq4F04D9KqX9YqxcCHax1Jd5+UlMIIZobh8aJa60nVSsaZy0vB+53ckxCCCEcJLMYCiGED5MkLoQQPkySuBBC+DBJ4kII4cMkiQshhA9zaAIsIYQQ3kla4kII4cMkiQshhA+TJC6EED5MkrgQQvgwSeJCCOHDJIkLIYQPkyQuhBA+zOuSuFIq3Hrb8FUb3EgpFevpGBwhcTqXL7w+fSFGkDidxWsu9lFK3QD8BjgCTNdaH/FwSLVSSl0D/A44BcwEtmmtSzwbVU0Sp3P5wuvTF2IEidPZvKklfjfwNrAVeFAp1d/D8dRlJMYKRvOBwcAdng2nThKnc/nC69MXYgSJ06kcWhTCFZRSYRhv4O+A48AhYAOwylp+mVLqZ09/+imlQoFngSVa62+A/RgLR28ASoAhSqlUrfVOD4Zpi/NNYLvW+i/AYbwzzjDgF8AOrfWPwAHgGLAe74uzC5AFFGC0xrzq9WmN8SKM906eN8YIoJQKBkK11nlKqdZ473s9DEjFeE2e4fz7x6virM4jLXGl1C+Br4EwYJ/WOh9oA1yptS4FfgRCMBZf9hilVHuMFuJxwLb0nAnohLFe6HaMF2RXjwRopZS6F/gP0Bq4xVpchvfFeQXwA9AfeFEplQSY8b44r8R4Dd4PzAX8gRjgCm95fSql+gCbMGJ8H6NBFgNc7i0xgv29vh14AUBrfRxoi/fF+SuMBuUDwAda6wogAi/LSbVxexJXSkUBI4DngRXA9UqpeODvwANKqXCt9VYgBejo7viqCQA+x/gkfkQplQEsBTKAdK11DsY/NhQ8c+JDKRWD0WL8ndb6F8A6pVQkxuLV/+ctcVqlAy9prR/CeFP0BxZ5U5zWY14KTNRa/w7jQ+VJ4L/AGC96fXYA/qC1ngjsAO7FeG16TYxKqRAgHKNf2aSUGmKt+gz4tVIqzEvijAb6APdprccD2UqpYRgNuDHeEmdd3NKdopTqADyB8YZdA8wBHgeCgC+APwHXYHylnmhdeDkEowXkNpXi/ByjHywOuAEj+WwC/gi8AvwMDFNKpWH88/MBtNZuOUtc7e+5RWv9B2t5CnAJEKC1Xq+UugQY7gVxLuR8d8mlSqlDGH/TAOB74CfgdqVUuhfEuQ6jcTMc+BJ4GdgC9AU246HXp/V/+6Q1xg0YLcKrgY8wXpOvA9OssXryPWSLcxHwk9b6bWt5KPBLpdQSrfVipdTVGA2jdR6OczGwUWv9RKVGw1bgnNZ6rVLqVk/G6QiXt8SVUskYL7CDGF0m72qtF2Mk7mu11q8B7wKvaq2nYyT5cRgvgIWujq+OOJOAt7TWGiORl2qt51nrbwDeAz7F+OD5wfZC9UCcicBsW53W+iBGP95oa9G7wMdeEGdb4F/Wv+F8jK/WmzFakJMxvjV8ipGUPBlnO+AdrfXfgFSl1J3AXcBO4F6t9QsYX7nd+vpUSgVinJc5zPnX5ntAJ6VUhtb6JMaH4eNa6+fx3HuocpxtMEYb2awETmC0ygFes8bs6ThbA7OgSqOhE+cbuNM9FaejXDbE0PpJuxrjRfeO1nqQtXw5sFJr/bJSyl9rXaGUCgD+DTyotS5QSgVorctdEpjjca7ASDh7gala6+us5f8Dfqu1/tkWv4fjXAqstiYYlFI3AWnAXwCz1triJXEuxzgP8jLwX631rdbyD4EXtdZbvCRO2/99KcZomSiM0TPjMIaZlbvr9Wn9Sh8PfAW8rbUeaC1/1xpfFvCk1nqotXwu8KjWOt/N76G64vwnxgns16yt3BTgRYxvZSu11lu9KM7tWuvXrd29z1h/hlnj3+LOOBvK6S1xpVSE9Q27AuNNcAr4Tik1zrrLauBWpVSMNYFfjdHfuBcoBHDTG+RCcX4LjLHefqeU+oN1/yPWfXFHwnEgzjUYIzpirPdTgI5a6wpby8JL4lwNDNFamwE/pdRUpdRijC6WE14Up+3/XqC1/gfGCeO/Avm216WrX59KqQSl1EKMc0dpwPXACaWU7RvWVGACRgvxqFJqilLqW4xRFQXuiNHBOP+I0Z2XYH0tRgFXYIxOKvOyOO9USiVg/P0GYHSzjMJ4fbolzsZySUtcKXUZ0B7oh/GVKsZ6ewAjURdhfBpuw+gf/5fW+j9OD6TpcZ7D+IBZh/G1K11rvcwL4ywCvrH2NbbCGEWx2AvjLMXow92LcfIwykv/70UYX//XYlyItFVr/Ykb44vHGBXxhXV7CsaH9RXALK31HqWUrUW7HOPEdmet9efuirEBcf4R2AN8iJEUT2ut/+uFcU7DyEcbgCXAY1rrJe6Ms7FccmJTa70R2KiUGojR7/2+MoYaRWG8gSdinJA7Bgyp+5lcy8E4d1k/hbOtP94ap7bum4vRivDWOA9YR6Es90SMDYgzU2tdADzngRBzgGXWWE8ppdpgtBD3YHyoPAhEYgzPLcY4EbfVS+OMBTZb30PveCBGR+OMweg62Q9091CcjeKSE5uVzvLOA65VSiVqrY9inFH/GOMrf4FSyqNXjDoYZ6Hy8JwJzSzOAh+J02N/T621RWt9TillsnaTRWutl2itZ2EM1XsX46T2IU/E14g4D3ryf96AOA94+rXZGC6fO0Up9QjG173TGEPzdmutN7j0oI0gcTqXxNl01oQSh3FSdQlwE0Y35G6t9WlPxlaZxOlZLmsJV2pl9wJuxvjq9763vEFsJE7nkjidx3oysC9Gl86LwCGt9XpvSzgSp2e57GIf6ygEMD7xJmitz7nqWE0hcTqXxOl0ucDvgb9o4/JvbyVxeojXTEUrhKhJKWXSbrpytSkkTs+RJC6EED7Mm+YTF0II0UCSxIUQwodJEhdCCB8mSVwIIXyYx5ZnE8LdlFJPYEyBPFprPbeOfcIw5pk+UNc+QngTaYkLUVUY8AeMZc+E8HoyxFA0a9bW92SM6W5/wFjGbDTGxGvXYywFtw94Rmv9mVLqAMbcKTZ/BF6y/vwSY7mx5cB4bSzGIIRHSRIXzZZSqjeQiTHF6F8xWtjtMJJ4IsZ8KREYq820BxIwFgKYh7Hq0DSM2QHvwLhU+x/AMYyl3JZqre9w2y8jRB2kT1w0ZwOst29orf+plGqPMZe0P8Yan3dhrPNq0xHrlKXACa31hwBKKdsUquMq7TvIRTEL0SCSxEVLYKp2G4jRrfIVxlqPj2B0r4QAdX01LQeGArbVh+R8kvAKksRFc/a19XaiddbC0dXqw4FuwFWVyvIBM9BVKXUPxsLICwEF3IeR+NMwFtN1+ypPQlQnrQnRbGmtNwOTMFZefwT4xlpVhrFcWB+MLpWllR5ThjEMMQZ4H+iPsbjzn6zbMzCmrrU9lxAeJSc2hRDCh0lLXAghfJgkcSGE8GGSxIUQwodJEhdCCB8mSVwIIXyYJHEhhPBhksSFEMKHSRIXQggf9v8jZZtJmy3O9AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Temporalne Konvolucijske Mreže model**"
      ],
      "metadata": {
        "id": "iMkvecCdaVah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_air = TCNModel(\n",
        "    input_chunk_length=13,\n",
        "    output_chunk_length=12,\n",
        "    n_epochs=500,\n",
        "    dropout=0.1,\n",
        "    dilation_base=2,\n",
        "    weight_norm=True,\n",
        "    kernel_size=5,\n",
        "    num_filters=3,\n",
        "    random_state=0,\n",
        ")\n",
        "\n",
        "model_air.fit(\n",
        "    series=train,\n",
        "    val_series=val,\n",
        "    verbose=True,\n",
        ")"
      ],
      "metadata": {
        "id": "l65a8B9qlkhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model_air.predict(len(val))\n",
        "\n",
        "print(\n",
        "    \"Mean absolute percentage error for the combined naive drift + seasonal: {:.2f}%.\".format(\n",
        "        mape(val, forecast)\n",
        "    )\n",
        ")\n",
        "train.plot()\n",
        "val.plot()\n",
        "forecast.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
          "referenced_widgets": [
            "80ea662a4bb3400c95098d8e0633ee0e",
            "83d3f9bc68df48318ef4418222cbcf53",
            "bea95a93594a44c7bdc10a6f536156aa",
            "79c61e466d6e4b4aa83b267b07d0a05c",
            "fa815538d5b24b7ebfcdc1265250d4f4",
            "0a7f02fc1c6545619b6a2fc08a206b8e",
            "79d898659e364751879f4ea96e883e9e",
            "1ff07d94ca684497b342da131fa51225",
            "fc4ec6f25b614c21ba8901b17e5ef27b",
            "b3a4b1f2c62547a3bc4c591132c4ef73",
            "c8bfd509c5d645c39aadd4593ed2149c"
          ]
        },
        "id": "wcrsgUpFnhaK",
        "outputId": "2b415911-c032-483c-c8ce-d0c5be870f63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 24it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80ea662a4bb3400c95098d8e0633ee0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error for the combined naive drift + seasonal: 27.20%.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}