{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "title: \"Lab 03: TimeGPT\"\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "from dotenv import load_dotenv\n",
        "from nixtla import NixtlaClient\n",
        "import pandas as pd\n",
        "\n",
        "load_dotenv()\n",
        "\n",
        "nixtla_client = NixtlaClient(api_key=os.getenv(\"NIXTLA_KEY\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:nixtla.nixtla_client:Happy Forecasting! :)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nixtla_client.validate_api_key()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>id</th>\n",
              "      <th>y</th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>9580.0</td>\n",
              "      <td>2018-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>9380.0</td>\n",
              "      <td>2018-04-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>9450.0</td>\n",
              "      <td>2018-04-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>9490.0</td>\n",
              "      <td>2018-04-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>9740.0</td>\n",
              "      <td>2018-04-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id       y         ds\n",
              "0   1  9580.0 2018-04-01\n",
              "1   2  9380.0 2018-04-02\n",
              "2   3  9450.0 2018-04-03\n",
              "3   4  9490.0 2018-04-04\n",
              "4   5  9740.0 2018-04-05"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_intermediario = pd.read_csv(\"../data/dados_amostra.csv\", header=None, names=[\"id\", \"y\"])\n",
        "# transform ds column to date starting on 2018-01-01 and daily frequency\n",
        "df_intermediario[\"ds\"] = pd.date_range(start=\"2018-04-01\", periods=len(df_intermediario), freq=\"D\")\n",
        "df_intermediario.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1062, 3)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_intermediario.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:nixtla.nixtla_client:Validating inputs...\n",
            "c:\\Users\\jtrec\\Documents\\insper\\202534-fa\\.venv\\Lib\\site-packages\\nixtla\\nixtla_client.py:380: UserWarning: `df` contains the following exogenous features: ['id'], but `X_df` was not provided and they were not declared in `hist_exog_list`. They will be ignored.\n",
            "  warnings.warn(\n",
            "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
            "INFO:nixtla.nixtla_client:Querying model metadata...\n",
            "WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon, this may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
            "INFO:nixtla.nixtla_client:Restricting input...\n",
            "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
          ]
        }
      ],
      "source": [
        "fcast = nixtla_client.forecast(\n",
        "    df=df_intermediario,\n",
        "    h=18,\n",
        "    time_col=\"ds\",\n",
        "    target_col=\"y\",\n",
        "    freq=\"D\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>ds</th>\n",
              "      <th>TimeGPT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>9395.520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-02-27</td>\n",
              "      <td>9374.905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-02-28</td>\n",
              "      <td>9370.457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-03-01</td>\n",
              "      <td>9380.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-03-02</td>\n",
              "      <td>9394.539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021-03-03</td>\n",
              "      <td>9391.961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021-03-04</td>\n",
              "      <td>9387.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021-03-05</td>\n",
              "      <td>9395.285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021-03-06</td>\n",
              "      <td>9406.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021-03-07</td>\n",
              "      <td>9417.757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>9429.612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2021-03-09</td>\n",
              "      <td>9442.144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>9426.731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2021-03-11</td>\n",
              "      <td>9409.467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2021-03-12</td>\n",
              "      <td>9413.079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2021-03-13</td>\n",
              "      <td>9425.132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2021-03-14</td>\n",
              "      <td>9432.346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2021-03-15</td>\n",
              "      <td>9437.424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds   TimeGPT\n",
              "0  2021-02-26  9395.520\n",
              "1  2021-02-27  9374.905\n",
              "2  2021-02-28  9370.457\n",
              "3  2021-03-01  9380.596\n",
              "4  2021-03-02  9394.539\n",
              "5  2021-03-03  9391.961\n",
              "6  2021-03-04  9387.700\n",
              "7  2021-03-05  9395.285\n",
              "8  2021-03-06  9406.790\n",
              "9  2021-03-07  9417.757\n",
              "10 2021-03-08  9429.612\n",
              "11 2021-03-09  9442.144\n",
              "12 2021-03-10  9426.731\n",
              "13 2021-03-11  9409.467\n",
              "14 2021-03-12  9413.079\n",
              "15 2021-03-13  9425.132\n",
              "16 2021-03-14  9432.346\n",
              "17 2021-03-15  9437.424"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fcast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# transform column ds back to integer\n",
        "fcast['id'] = (fcast['ds'] - pd.Timestamp(\"2018-04-01\")).dt.days\n",
        "fcast['y'] = fcast['TimeGPT']\n",
        "fcast[['id', 'y']].to_csv(\"../data/forecast_timegpt.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1600x350 with 1 Axes>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fcast_pred = fcast[['ds', 'TimeGPT']].copy()\n",
        "df_intermediario_plot = df_intermediario[['ds', 'y']].copy()\n",
        "nixtla_client.plot(\n",
        "    df_intermediario_plot, \n",
        "    fcast_pred, \n",
        "    time_col=\"ds\", \n",
        "    target_col=\"y\"\n",
        ")\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3",
      "path": "C:\\Users\\jtrec\\Documents\\insper\\202534-fa\\.venv\\share\\jupyter\\kernels\\python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}