notebook/demand_forecasting.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Projeto Adventure Works - Previsão de Demanda dos Produtos por Loja Consumidora"
      ],
      "metadata": {
        "id": "MahHs3E_a2AR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introdução e objetivos"
      ],
      "metadata": {
        "id": "Gbn1DYn1mcz4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A fim de ajustar a distribuição de produtos e ter uma melhor estimativa sobre a necessidade de compra de matéria prima, a empresa Adventure Works gostaria de ter uma previsão sobre a demanda dos próximos 3 meses de cada produto em cada loja.\n",
        "\n",
        "Como o objetivo é realizar a previsão de um número, que é a demanda de produtos, este problema poderia ser considerado ser abordado com modelos de regressão. Contudo, não se trata de um problema de regressão simples, pois a ideia seria prever demanda ao longo do tempo, ou seja, tem-se aplicações de séries temporais.\n",
        "\n",
        "Para cada produto, em cada loja, em cada província/estado e país, existe uma série temporal diferente. Portanto, observando esta questão de hierarquia (produto, loja, estado , país) pode-se aplicar modelos de séries temporais hierárquicas.\n",
        "\n",
        "Desta forma, o presente projeto abordará o problema de negócio \"previsão de demanda por produto por loja\" seguindo duas linhas diferentes:\n",
        "\n",
        "- Modelos de Séries Temporais Hierárquicas\n",
        "- Modelo de Regressão\n",
        "\n",
        "Ao fim, as métricas obtidas com cada um deles serão comparadas."
      ],
      "metadata": {
        "id": "VCxvjEIrj0uz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instalando e importando as bibliotecas necessárias"
      ],
      "metadata": {
        "id": "OjqabpG8AnyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install hierarchicalforecast"
      ],
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        "id": "o3sh2yOCtMdp",
        "outputId": "3b328005-d20c-4613-a468-d67d186fb066"
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      "execution_count": 173,
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install statsforecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VX5l4Ua1tcGI",
        "outputId": "4ad7e5cf-74cc-4486-d4e8-b7a0195844e3"
      },
      "execution_count": 174,
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    {
      "cell_type": "code",
      "source": [
        "# libs para conexão a base de dados salva no Big Query da Google\n",
        "from google.cloud import bigquery\n",
        "from google.colab import auth\n",
        "\n",
        "# libs para manipulação dos dados\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# libs para plotagem de dados\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# libs de séries temporais hierárquicas\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose, DecomposeResult\n",
        "from statsforecast.models import AutoARIMA, SeasonalNaive\n",
        "from statsforecast.core import StatsForecast\n",
        "from hierarchicalforecast.utils import aggregate\n",
        "\n",
        "# libs de regressão\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "import xgboost as xgb\n",
        "\n",
        "# libs para avaliação de métricas\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# lib utilizada para ignorar warnings ao longo da execução do código\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "tY5dzz8HTg2p"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo as funções de métricas a serem utilizadas no decorrer do projeto"
      ],
      "metadata": {
        "id": "s80ULtUhYhtP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Foram definidas abaixo as funções customizadas para cálculo das métricas, tanto dos modelos de Séries Temporais Hierárquicas, quanto de Regressão.\n",
        "\n",
        "- MAE = erro médio absoluto, basicamente mede a distância média entre os valores previstos pelo modelo e os valores reais.\n",
        "- MSE = erro médio quadrático, basicamente é a diferença entre o valor previsto e o valor real, elevando-se o resultado ao quadrado. Da mesma forma que o MAE, quanto maior esta métrica pior será o modelo.\n",
        "- RMSE = raiz do erro quadrático médio, basicamente é a métrica MSE tirando a sua raiz quadrada. Assim, tem-se um valor de melhor interpretabilidade, corringindo também as unidades deste erro."
      ],
      "metadata": {
        "id": "6odpLyF8YnNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# cálculo do mae\n",
        "def custom_mae(y, y_pred):\n",
        "    return np.round(np.nanmean(abs(y.values -  y_pred.values)), 2)\n",
        "\n",
        "# cálculo do mse\n",
        "def custom_mse(y, y_pred):\n",
        "    return np.round(mean_squared_error(y.values, y_pred.values), 2)\n",
        "\n",
        "# cálculo do rmse\n",
        "def custom_rmse(y, y_pred):\n",
        "    return np.round( np.sqrt(mean_squared_error(y.values,y_pred.values)), 2)"
      ],
      "metadata": {
        "id": "2EUK4dm0Y0QM"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Leitura dos dados de interesse no Big Query"
      ],
      "metadata": {
        "id": "FsbIRW6VGsJ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como primeira etapa do projeto, é necessário realizar a leitura dos dados salvos no Big Query. Para tal, precisa-se realizar a autentificação do acesso a conta em que se executa este notebook, de tal forma que Google Colab possa acessar arquivos dentro do Google Cloud.\n",
        "\n",
        "Isto é realizado a partir do código abaixo."
      ],
      "metadata": {
        "id": "QsL7_fdQL7HZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()"
      ],
      "metadata": {
        "id": "14z50BKcKlab"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feito, criou-se um agente para realizar as leituras no BigQuery, informando o nome do projeto,"
      ],
      "metadata": {
        "id": "5SZ_H5mwMKov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client = bigquery.Client(project='adw-project-431916')"
      ],
      "metadata": {
        "id": "2C70hOyZHuVV"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Passando agora as informações do id do projeto, id do schema e id da tabela que será necessário selecionar,"
      ],
      "metadata": {
        "id": "_VD6RkM2MTL6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "project_id = 'adw-project-431916'\n",
        "dataset_id = 'dbt_rodrigoprates_marts'\n",
        "table_id = 'agg_product_store'"
      ],
      "metadata": {
        "id": "3ANIGvrmHz3x"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Especificando a consulta SQL que será executa pelo agente, no qual irá selecionar todos os dados presentes dentro da \"table_id\" especificada acima,"
      ],
      "metadata": {
        "id": "80Cw9JqHMo-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Query SQL ara carregar os dados da tabela \"agg_product_store\"\n",
        "query = f\"\"\"\n",
        "SELECT *\n",
        "FROM {project_id}.{dataset_id}.{table_id}\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "046erzHXMkwT"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por fim, salvando os resultados da consulta especificada acima dentro de um objeto pandas data frame."
      ],
      "metadata": {
        "id": "Txf3NH_WNGJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = client.query(query).to_dataframe()"
      ],
      "metadata": {
        "id": "FmAaq8gtNOH6"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explorando a base de dados"
      ],
      "metadata": {
        "id": "mShkRhWMUT9B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mostrando as primeiras 5 linhas do Data Frame,"
      ],
      "metadata": {
        "id": "40CWumWiNUQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UuhbrRCTNYrR",
        "outputId": "842f08ef-9448-4971-b00d-35dbabb6f03d"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  orderdate                store_name                product  \\\n",
              "0 2011-05-31 00:00:00+00:00              Great Bikes   Sport-100 Helmet, Red   \n",
              "1 2011-05-31 00:00:00+00:00  Bike Dealers Association       Road-650 Red, 62   \n",
              "2 2011-06-22 00:00:00+00:00               Online Sell     Road-650 Black, 52   \n",
              "3 2011-08-01 00:00:00+00:00            Riders Company       Road-650 Red, 60   \n",
              "4 2011-08-31 00:00:00+00:00         Bike Rims Company       Road-150 Red, 62   \n",
              "\n",
              "      province        country  orderqty  \n",
              "0      Wyoming  United States         3  \n",
              "1   New Mexico  United States         3  \n",
              "2      Moselle         France         1  \n",
              "3    Minnesota  United States         3  \n",
              "4  Mississippi  United States         2  "
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              "      <td>New Mexico</td>\n",
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              "      <td>Online Sell</td>\n",
              "      <td>Road-650 Black, 52</td>\n",
              "      <td>Moselle</td>\n",
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              "      <td>2011-08-01 00:00:00+00:00</td>\n",
              "      <td>Riders Company</td>\n",
              "      <td>Road-650 Red, 60</td>\n",
              "      <td>Minnesota</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2011-08-31 00:00:00+00:00</td>\n",
              "      <td>Bike Rims Company</td>\n",
              "      <td>Road-150 Red, 62</td>\n",
              "      <td>Mississippi</td>\n",
              "      <td>United States</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dc97a74e-f5a4-4e0a-a443-81cac3676656 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-dc97a74e-f5a4-4e0a-a443-81cac3676656');\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",
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              "\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",
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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",
              "      --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",
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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",
              "  }\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",
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              "      border-color: transparent;\n",
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              "      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-017149ac-f6d5-4a18-be71-2ec0b5010a8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Percebe-se então que, dentro da tabela \"agg_product_store\", presente dentro do schema \"dbt_rodrigoprates_marts\", salvo dentro do projeto Big Query \"adw-project-431916\", tem-se informações de data, produtos, lojas, província, país e demanda de cada produto.\n",
        "\n",
        "Mostrando mais informações a respeito deste Data Frame,"
      ],
      "metadata": {
        "id": "ediOA7yqNbeP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqmIPiJyK2Jx",
        "outputId": "331dbdfb-8624-452b-ddc3-68af9be38f29"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 121317 entries, 0 to 121316\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count   Dtype              \n",
            "---  ------      --------------   -----              \n",
            " 0   orderdate   121317 non-null  datetime64[us, UTC]\n",
            " 1   store_name  121317 non-null  object             \n",
            " 2   product     121317 non-null  object             \n",
            " 3   province    121317 non-null  object             \n",
            " 4   country     121317 non-null  object             \n",
            " 5   orderqty    121317 non-null  Int64              \n",
            "dtypes: Int64(1), datetime64[us, UTC](1), object(4)\n",
            "memory usage: 5.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisando-se os dados, o primeiro ponto importante a ser avaliado é que nenhuma das colunas apresenta dados nulos. O Data Frame apresenta 121317 entradas, ou linhas, e todas as colunas tem estas 121317 linhas preechidas com valores não nulos. Esta avaliação é muito importante pois, como a ideia deste projeto é realizar previsões de demanda utilizando modelos de série temporal hierárquica e modelos de regressão, ambas estas famílias de modelos não se comportam bem com dados nulos.\n",
        "\n",
        "Outro ponto importante se refere aos tipos de dados de cada coluna. Percebe-se que a coluna de data \"orderdate\" está especificada com o tipo correto de dados (datetime). Porém, esta coluna, além de retornar dados de mês, dia e ano, conforme visto anteriormente, ela também tem os dados de hora, minuto e segundo. Como essas últimas informações não são relevantes para a análise aqui, pois o interesse está no dia em si, optou-se por formatar os dados desta coluna, conforme segue abaixo."
      ],
      "metadata": {
        "id": "ijs29g4IOKtX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['orderdate'] = pd.to_datetime(df['orderdate'], format='%Y-%m-%d %H:%M:%S').dt.date"
      ],
      "metadata": {
        "id": "6uoBo4a0R-SJ"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['orderdate'] = pd.to_datetime(df['orderdate'])"
      ],
      "metadata": {
        "id": "Wd0wbgPASLJA"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['orderdate']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "pD5anAPmTM-q",
        "outputId": "d0abac77-232f-4f1f-dc8e-5cd71aa89c3b"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2011-05-31\n",
              "1        2011-05-31\n",
              "2        2011-06-22\n",
              "3        2011-08-01\n",
              "4        2011-08-31\n",
              "            ...    \n",
              "121312   2014-04-25\n",
              "121313   2014-05-01\n",
              "121314   2014-05-01\n",
              "121315   2014-05-06\n",
              "121316   2014-05-12\n",
              "Name: orderdate, Length: 121317, dtype: datetime64[ns]"
            ],
            "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>orderdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-05-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-05-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-06-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-08-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121312</th>\n",
              "      <td>2014-04-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121313</th>\n",
              "      <td>2014-05-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121314</th>\n",
              "      <td>2014-05-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121315</th>\n",
              "      <td>2014-05-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121316</th>\n",
              "      <td>2014-05-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>121317 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> datetime64[ns]</label>"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feitos estes devidos tratamentos iniciais, verifica-se abaixo a nova \"cara\" destes dados,"
      ],
      "metadata": {
        "id": "DNwIjOe2ZGMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glQkZI6HZFjK",
        "outputId": "d48f38e9-1f14-4779-911f-a25cff357865"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 121317 entries, 0 to 121316\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count   Dtype         \n",
            "---  ------      --------------   -----         \n",
            " 0   orderdate   121317 non-null  datetime64[ns]\n",
            " 1   store_name  121317 non-null  object        \n",
            " 2   product     121317 non-null  object        \n",
            " 3   province    121317 non-null  object        \n",
            " 4   country     121317 non-null  object        \n",
            " 5   orderqty    121317 non-null  Int64         \n",
            "dtypes: Int64(1), datetime64[ns](1), object(4)\n",
            "memory usage: 5.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando agora o gráfico de demandas ao longo do tempo,"
      ],
      "metadata": {
        "id": "bQjYdNk8ecOc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "sns.lineplot(data = df, x = 'orderdate', y = 'orderqty', color = 'blue', errorbar = None)\n",
        "plt.title('Demanda ao longo do tempo')\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Demanda')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "yNyMCxNKd_Nt",
        "outputId": "0c3ee1e3-ed69-4891-ab07-4422ca556ec2"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Percebe-se que as demandas no gráfico acima estão expostas em dias. Para mostrar estes valores por mês, primeiro é necessário criar uma nova coluna no dataset, de forma a retirar o mês de cada data, conforme demonstrado abaixo,"
      ],
      "metadata": {
        "id": "ABCcC0cJfzSN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['month'] = df['orderdate'].dt.to_period('M').dt.to_timestamp()"
      ],
      "metadata": {
        "id": "d8pso7gMf7r7"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LRDZi7npgFiJ",
        "outputId": "763baeaf-b4fb-4b98-a804-bf20ce9259bb"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   orderdate                store_name                product     province  \\\n",
              "0 2011-05-31              Great Bikes   Sport-100 Helmet, Red      Wyoming   \n",
              "1 2011-05-31  Bike Dealers Association       Road-650 Red, 62   New Mexico   \n",
              "2 2011-06-22               Online Sell     Road-650 Black, 52      Moselle   \n",
              "3 2011-08-01            Riders Company       Road-650 Red, 60    Minnesota   \n",
              "4 2011-08-31         Bike Rims Company       Road-150 Red, 62  Mississippi   \n",
              "\n",
              "         country  orderqty      month  \n",
              "0  United States         3 2011-05-01  \n",
              "1  United States         3 2011-05-01  \n",
              "2         France         1 2011-06-01  \n",
              "3  United States         3 2011-08-01  \n",
              "4  United States         2 2011-08-01  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agrupando agora as demandas por mês,"
      ],
      "metadata": {
        "id": "7OCAJNbbgS7h"
      }
    },
    {
      "cell_type": "code",
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        "monthy_demand = (\n",
        "    df.groupby('month')['orderqty']\n",
        "    .sum()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "monthy_demand.head(10)"
      ],
      "metadata": {
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          "height": 363
        },
        "id": "keklloTxgHIx",
        "outputId": "234a904d-1589-4188-804e-25eb2f4e5e58"
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      "execution_count": 192,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       month  orderqty\n",
              "0 2011-05-01       825\n",
              "1 2011-06-01       141\n",
              "2 2011-07-01      2209\n",
              "3 2011-08-01      2904\n",
              "4 2011-09-01       157\n",
              "5 2011-10-01      5382\n",
              "6 2011-11-01       230\n",
              "7 2011-12-01      1040\n",
              "8 2012-01-01      3967\n",
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              "        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-4291dac9-fe73-4b2a-8cce-ac480b4f4995\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4291dac9-fe73-4b2a-8cce-ac480b4f4995')\"\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-4291dac9-fe73-4b2a-8cce-ac480b4f4995 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "monthy_demand",
              "summary": "{\n  \"name\": \"monthy_demand\",\n  \"rows\": 38,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orderqty\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          4287,\n          15884,\n          157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotando novamente o gráfico,"
      ],
      "metadata": {
        "id": "jt3mwm2ngWAD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data = monthy_demand, x='month', y='orderqty', color='blue', errorbar = None)\n",
        "plt.title('Demanda ao longo do tempo')\n",
        "plt.xlabel('Mês')\n",
        "plt.ylabel('Demanda')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "9JC-_8l1gNwJ",
        "outputId": "63d55f3b-02f1-4bf3-8bca-7b5b14f4434d"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizando-se agora uma decomposição desta série agrupada mensalmente, com o intuito de avaliar sazonalidade, tendência e resíduos."
      ],
      "metadata": {
        "id": "aQZaEuNAgyB1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = seasonal_decompose(monthy_demand.set_index('month')['orderqty'], model='additive', period = 12)"
      ],
      "metadata": {
        "id": "n2Q7zaE7hJRz"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotando-se os gráficos abaixo das decomposições realizadas. Para tal, definiu-se uma função para exibir cada um dos 4 gráficos e uma única figura."
      ],
      "metadata": {
        "id": "kibj5Obxiptl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_decomposition(data: pd.DataFrame, result: DecomposeResult):\n",
        "\n",
        "    plt.figure(figsize=(12, 8))\n",
        "\n",
        "    # Plotando a série temporal agrupada por mês sem nenhum tipo de decomposição\n",
        "    plt.subplot(4, 1, 1)\n",
        "    plt.plot(data['orderqty'], label = 'Original')\n",
        "    plt.legend(loc='upper left')\n",
        "\n",
        "    # Plotando o gráfico de Tendência da série temporal\n",
        "    plt.subplot(4, 1, 2)\n",
        "    plt.plot(result.trend, label='Trend', color = 'orange')\n",
        "    plt.legend(loc='upper left')\n",
        "\n",
        "    # Plotando o gráfico de Sazonalidade da série temporal\n",
        "    plt.subplot(4, 1, 3)\n",
        "    plt.plot(result.seasonal, label='Seasonal', color = 'green')\n",
        "    plt.legend(loc='upper left')\n",
        "\n",
        "    # Plotando o gráfico de Resíduos da série temporal\n",
        "    plt.subplot(4, 1, 4)\n",
        "    plt.plot(result.resid, label='Residual', color = 'red')\n",
        "    plt.legend(loc='upper left')\n",
        "\n",
        "    plt.tight_layout()\n",
        "\n",
        "    return plt.show()"
      ],
      "metadata": {
        "id": "nBL95qhEhcLx"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decomposition(data = monthy_demand, result = result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "HhmQLvuIhi4B",
        "outputId": "b901c03a-7d3d-43a0-9b50-1d5ffb2cb8a5"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observando os resultados acima, é possível notar alguns pontos:\n",
        "\n",
        "* Como demonstra o gráfico em laranja, existe uma tendência de aumento da demanda de produtos pelas lojas ao passar do tempo.\n",
        "* Pelo gráfico em verde, percebe-se uma tendência sazonal de aproximadamente 1 ano, com repetição dos comportamentos de demanda, principalmente do mês de setembro de 2011 em diante.\n",
        "* Já o gráfico de resíduos não nos diz muita coisa, com um comportamento randômico ao longo do tempo.\n",
        "\n"
      ],
      "metadata": {
        "id": "EYfsQxBs7lXm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Previsão de demanda utilizando modelos de Série Temporal Hierárquica"
      ],
      "metadata": {
        "id": "fiqejRRN8lhE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Considerando o objetivo do projeto, que é prever a demanda por produto para cada loja, a primeira abordagem de modelagem testada foi utilizando a aplicação de séries temporais.\n",
        "\n",
        "Para tal, primeiro agregou-se a soma das demandas por mês, produto, país, província e loja, conforme demonstrado abaixo."
      ],
      "metadata": {
        "id": "S-aQSKFF4oj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hts = (\n",
        "    df.groupby(['month', 'product', 'country', 'province', 'store_name'])['orderqty']\n",
        "    .sum()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "hts.head()"
      ],
      "metadata": {
        "id": "ecM0ExLQiJ5S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c7173f3d-dc96-49b1-a1de-f5bad3bc215b"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       month       product        country          province  \\\n",
              "0 2011-05-01  AWC Logo Cap         Canada  British Columbia   \n",
              "1 2011-05-01  AWC Logo Cap         Canada           Ontario   \n",
              "2 2011-05-01  AWC Logo Cap         Canada            Quebec   \n",
              "3 2011-05-01  AWC Logo Cap  United States        California   \n",
              "4 2011-05-01  AWC Logo Cap  United States        California   \n",
              "\n",
              "                        store_name  orderqty  \n",
              "0                      Retail Mall         4  \n",
              "1  Original Bicycle Supply Company         4  \n",
              "2          Superb Sales and Repair         5  \n",
              "3            Area Bike Accessories         3  \n",
              "4       Separate Parts Corporation         3  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hts",
              "summary": "{\n  \"name\": \"hts\",\n  \"rows\": 77722,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 266,\n        \"samples\": [\n          \"LL Touring Frame - Yellow, 44\",\n          \"Road-250 Black, 58\",\n          \"Front Derailleur\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Canada\",\n          \"United States\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Victoria\",\n          \"British Columbia\",\n          \"Nord\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 634,\n        \"samples\": [\n          \"Self-Contained Cycle Parts Company\",\n          \"Hobby Store\",\n          \"Real Sporting Goods\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orderqty\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          2,\n          48,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definindo agora as hierarquias adotadas. Em um nível mais alto, considerou-se as previsões para os países, em seguida províncias, lojas e por último produtos."
      ],
      "metadata": {
        "id": "GD1DooU3-b6k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hierarchies = [\n",
        "    ['country'],\n",
        "    ['province'],\n",
        "    ['country', 'province'],\n",
        "    ['country', 'province', 'store_name'],\n",
        "    ['country', 'province', 'store_name', 'product']\n",
        "]"
      ],
      "metadata": {
        "id": "7ACX2euYshFQ"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observação importante: para a utilização das bibliotecas hierarchicalforescast e statsforecast, é necessário renomear as colunas de data para 'ds' e a coluna a ser prevista para 'y', conforme demonstrado a seguir."
      ],
      "metadata": {
        "id": "epTKutJq_dEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hts = hts.rename(columns={'month': 'ds', 'orderqty': 'y'})"
      ],
      "metadata": {
        "id": "XPenuOS4_bqK"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, antes da definição e aplicação dos modelos, é necessário agregar os dados conforme a hierarquia definida anteriormente. Para tal, utilizou-se da função aggregate() da biblioteca hierarchicalforecast."
      ],
      "metadata": {
        "id": "FxB1hRNQANFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_df, S_df, tags = aggregate(hts, hierarchies)\n",
        "Y_df = Y_df.reset_index()\n",
        "Y_df"
      ],
      "metadata": {
        "id": "ChskSQ5s_3B0",
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          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d4c8b517-0ff4-4f41-d353-4c0f57a1ea87"
      },
      "execution_count": 200,
      "outputs": [
        {
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              "                                               unique_id         ds   y\n",
              "0                                              Australia 2011-05-01   1\n",
              "1                                              Australia 2011-06-01  61\n",
              "2                                              Australia 2011-07-01  68\n",
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              "4                                              Australia 2011-09-01  64\n",
              "...                                                  ...        ...  ..\n",
              "86448  United States/Wyoming/Some Discount Store/Moun... 2011-10-01   2\n",
              "86449  United States/Wyoming/Some Discount Store/Moun... 2012-01-01   2\n",
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              "86451  United States/Wyoming/Some Discount Store/Wome... 2013-09-01   1\n",
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              "    <tr>\n",
              "      <th>86448</th>\n",
              "      <td>United States/Wyoming/Some Discount Store/Moun...</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>86449</th>\n",
              "      <td>United States/Wyoming/Some Discount Store/Moun...</td>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86450</th>\n",
              "      <td>United States/Wyoming/Some Discount Store/Moun...</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86451</th>\n",
              "      <td>United States/Wyoming/Some Discount Store/Wome...</td>\n",
              "      <td>2013-09-01</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>86452</th>\n",
              "      <td>United States/Wyoming/Some Discount Store/Wome...</td>\n",
              "      <td>2013-12-01</td>\n",
              "      <td>1</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Y_df",
              "summary": "{\n  \"name\": \"Y_df\",\n  \"rows\": 86453,\n  \"fields\": [\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24440,\n        \"samples\": [\n          \"United States/Florida/Valuable Bike Parts Company/Patch Kit_8 Patches\",\n          \"United States/North Carolina/Exchange Parts Inc./LL Crankset\",\n          \"United States/Florida/Valuable Bike Parts Company/Full-Finger Gloves, L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 661,\n        \"samples\": [\n          385,\n          391,\n          366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observa-se que existem unique_id a nível de país, país/estado, país/estado/loja e país/estado/loja/produto, conforme determinado anteriormente. Explorando-se mais os dados desta coluna,"
      ],
      "metadata": {
        "id": "pUmFA6g3B71w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_df['unique_id'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "EA6C75TZqNHS",
        "outputId": "23def60b-605f-4c4a-cf2e-303c3168ede2"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unique_id\n",
              "Australia                                                  38\n",
              "New South Wales                                            38\n",
              "United States/California/Online Sell                       38\n",
              "Canada/British Columbia/Online Sell                        38\n",
              "Canada                                                     38\n",
              "                                                           ..\n",
              "Germany/Saarland/Online Sell/Touring-3000 Blue, 50          1\n",
              "Germany/Saarland/Online Sell/Touring-3000 Blue, 58          1\n",
              "United States/Ohio/Bike Boutique/Road-350-W Yellow, 44      1\n",
              "Germany/Saarland/Online Sell/Touring-3000 Yellow, 44        1\n",
              "United States/South Dakota/Travel Systems/LL Road Pedal     1\n",
              "Name: count, Length: 24440, dtype: int64"
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              "      <th>Germany/Saarland/Online Sell/Touring-3000 Yellow, 44</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>24440 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_df['unique_id'].value_counts().describe()"
      ],
      "metadata": {
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        },
        "id": "945rhY1tqfxd",
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      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    24440.000000\n",
              "mean         3.537357\n",
              "std          3.376433\n",
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              "      <th>std</th>\n",
              "      <td>3.376433</td>\n",
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              "      <th>min</th>\n",
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              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observa-se que pelo menos 50% dos dados apresentam-se somente uma ocorrência no dataset. Ou seja, considerando a hierarquia definida anteriormente, existem mais de 12 mil linhas, ou combinações, dentro do dataset que acontecem somente uma vez. Isto pode representar compras esporádicas, produtos que foram comprados em um mês específico, em lojas, e/ou estados, e/ou países específicos.\n",
        "\n",
        "Este fenômeno, além de ser de difícil previsão, afeta as frequência de ocorrência que os modelos de séries temporais precisam captar ao avaliar e treinar com os dados. Desta forma, dependendo da forma como são dividos os dados, o modelo pode treinar com um dataset sem estas ocorrências específicas e no momento de teste/validação, aparecerem estes casos.\n",
        "\n",
        "Logo, para fins de solução deste projeto, optou-se por selecionar apenas os casos com ocorrência de ao menos 5 vezes na hierarquia determinada.\n",
        "\n",
        "Assim, filtrou-se abaixo o data frame utilizando esta condição,"
      ],
      "metadata": {
        "id": "PrEMtvoJqmJl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts = Y_df['unique_id'].value_counts()\n",
        "Y_df = Y_df[Y_df['unique_id'].isin(counts[counts > 5].index)]"
      ],
      "metadata": {
        "id": "JWijOk2M7D6f"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Partindo agora para a divisão dos dados, selecionou-se para teste os últimos 3 meses registrados nos dados."
      ],
      "metadata": {
        "id": "hqW4l7ws7bit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test_df = Y_df.groupby('unique_id').tail(3)\n",
        "Y_train_df = Y_df.drop(Y_test_df.index)"
      ],
      "metadata": {
        "id": "dB2aA6bUCX8v"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Treinando o modelo e previsão de demanda para os próximos 3 meses"
      ],
      "metadata": {
        "id": "G3HFJLrd7r70"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feito a divisão em bases de treino e teste, partiu-se para o treinamento dos modelos. Neste projeto, optou-se pela utilização de dois modelos: AutoARIMA e SeasonalNaive. Ambos foram declarados dentro do objeto sf, conforme demonstrado abaixo, com frequência de previsão mensal \"MS\", pois os dados foram agregados por mês."
      ],
      "metadata": {
        "id": "7hg8njpBEFlF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sf = StatsForecast(\n",
        "    df = Y_train_df,\n",
        "    models = [\n",
        "        AutoARIMA(),\n",
        "        SeasonalNaive(season_length=12)\n",
        "    ],\n",
        "    freq = 'MS',\n",
        "    n_jobs = -1\n",
        ")"
      ],
      "metadata": {
        "id": "NC-Eacb-EsOg"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definido o objeto sf acima, realizou-se o treinamento e previsão para os próximos 3 meses (h = 3), conforme demonstrado abaixo."
      ],
      "metadata": {
        "id": "cHfLjotxunQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_hat_df = sf.forecast(h = 3)"
      ],
      "metadata": {
        "id": "lIXMwCf1Eyk1"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com o dataset resultante, realizou-se um join entre este dataset e o dataset de teste, de forma a posteriormente serem avaliados."
      ],
      "metadata": {
        "id": "0kDkWjlsu0Te"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_merged = pd.merge(left = Y_test_df.set_index('unique_id').reset_index(), right = Y_hat_df.reset_index(), on = ['unique_id', 'ds'])"
      ],
      "metadata": {
        "id": "0NGbxTJZo-Jd"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "A11cNi2xpJGv",
        "outputId": "c61679c1-8446-4e25-d310-476e61d34f3b"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                              unique_id         ds     y  \\\n",
              "0                                             Australia 2014-04-01  1057   \n",
              "1                                             Australia 2014-05-01  2090   \n",
              "2                                             Australia 2014-06-01   357   \n",
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              "5138  United States/Wyoming/Great Bikes /Mountain-20... 2013-08-01     4   \n",
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              "5142  United States/Wyoming/Great Bikes /Sport-100 H... 2012-05-01     4   \n",
              "\n",
              "        AutoARIMA  SeasonalNaive  \n",
              "0     1676.000610          112.0  \n",
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              "2     1532.609741          778.0  \n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('Y_merged')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Y_merged",
              "summary": "{\n  \"name\": \"Y_merged\",\n  \"rows\": 5143,\n  \"fields\": [\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3206,\n        \"samples\": [\n          \"Australia/Queensland/Online Sell/Road-750 Black, 58\",\n          \"United States/Washington/Online Sell/Mountain Bottle Cage\",\n          \"United States/Missouri/World of Bikes/Long-Sleeve Logo Jersey, M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-11-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"2012-05-01 00:00:00\",\n          \"2012-10-01 00:00:00\",\n          \"2012-02-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 223,\n        \"samples\": [\n          560,\n          23,\n          542\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoARIMA\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 2187,\n        \"samples\": [\n          0.6811326742172241,\n          2.703346014022827,\n          1.8385260105133057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeasonalNaive\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 164,\n        \"samples\": [\n          42.0,\n          154.0,\n          175.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculando as métricas"
      ],
      "metadata": {
        "id": "jZ1FrK4s_B9X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizadas as previsões com o modelo de série temporal hierárquica definido anteriormente, analisou-se as métricas deles, tanto do AutoARIMA quando do SesonalNaive.\n",
        "\n",
        "Para tal, definiu-se a função declarada abaixo, de tal forma que ela irá validar, para cada um dos tags definidos anteriormente na hierarquia, a comparação entre o valor real \"y\" e o valor previsto, conforme o modelo especificado dentro da variável de entrada 'model'."
      ],
      "metadata": {
        "id": "WC1SSswYz29r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def metrics_results_hts(Y_merged: pd.DataFrame, model: str) -> pd.DataFrame:\n",
        "\n",
        "  results = []\n",
        "\n",
        "  for k in tags.keys():\n",
        "\n",
        "      rmse = custom_rmse(Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), 'y'], Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), model])\n",
        "      mse = custom_mse(Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), 'y'], Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), model])\n",
        "      mae = custom_mae(Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), 'y'], Y_merged.loc[Y_merged['unique_id'].isin(tags[k]), model])\n",
        "\n",
        "      results.append({'Category': k, 'RMSE': rmse, 'MSE': mse, 'MAE': mae})\n",
        "      results_df = pd.DataFrame(results)\n",
        "\n",
        "  return results_df"
      ],
      "metadata": {
        "id": "zwLgQpDb_Oou"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando os resultados para o modelo AutoARIMA,"
      ],
      "metadata": {
        "id": "UZK_fxif3IC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_auto_arima = metrics_results_hts(Y_merged, 'AutoARIMA')\n",
        "results_auto_arima"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HZpNKJhG221y",
        "outputId": "012a9b42-f441-4ec7-9cd6-7ebf1fd6df6f"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Category     RMSE         MSE      MAE\n",
              "0                              country  2041.05  4165876.13  1456.28\n",
              "1                             province   295.90    87554.26   156.72\n",
              "2                     country/province   295.90    87554.26   156.72\n",
              "3          country/province/store_name    90.69     8224.38    46.34\n",
              "4  country/province/store_name/product     4.19       17.60     2.77"
            ],
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              "summary": "{\n  \"name\": \"results_auto_arima\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"province\",\n          \"country/province/store_name/product\",\n          \"country/province\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 845.7450508457025,\n        \"min\": 4.19,\n        \"max\": 2041.05,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          295.9,\n          4.19,\n          2041.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1843011.707254459,\n        \"min\": 17.6,\n        \"max\": 4165876.13,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          87554.26,\n          17.6,\n          4165876.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614.4917228734656,\n        \"min\": 2.77,\n        \"max\": 1456.28,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          156.72,\n          2.77,\n          1456.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora os resultados para o modelo SeasonalNaive,"
      ],
      "metadata": {
        "id": "f5LjvuOm3M72"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_seasonal_naive = metrics_results_hts(Y_merged, 'SeasonalNaive')\n",
        "results_seasonal_naive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WENNrTLB26QP",
        "outputId": "8fab93c1-3094-4113-eed9-296a2b338055"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Category     RMSE         MSE      MAE\n",
              "0                              country  2160.45  4667523.61  1389.61\n",
              "1                             province   284.28    80812.83   159.04\n",
              "2                     country/province   284.28    80812.83   159.04\n",
              "3          country/province/store_name   138.14    19083.96    63.23\n",
              "4  country/province/store_name/product     5.31       28.24     2.61"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_seasonal_naive",
              "summary": "{\n  \"name\": \"results_seasonal_naive\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"province\",\n          \"country/province/store_name/product\",\n          \"country/province\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 894.1588232355591,\n        \"min\": 5.31,\n        \"max\": 2160.45,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          284.28,\n          5.31,\n          2160.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2067490.8959244608,\n        \"min\": 28.24,\n        \"max\": 4667523.61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          80812.83,\n          28.24,\n          4667523.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 582.3500897484261,\n        \"min\": 2.61,\n        \"max\": 1389.61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          159.04,\n          2.61,\n          1389.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Em ambos os casos, percebe-se uma diminuição do erro conforme a granularidade dos aumenta. Isto fica evidente pois, na mais baixa granularidade os dados de demanda a serem previstos também são menores. A demanda de um produto específico, em uma loja específica, em uma província específica de um país específico será menor do que a demanda agrupada para o país todo. Logo, os erros tendem a ser bem menores."
      ],
      "metadata": {
        "id": "_GyOgqR53yiz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analisando a previsão para os últimos 3 meses"
      ],
      "metadata": {
        "id": "OTnIGxb8CX7f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feitas as ponderações das métricas de cada modelo, plotou-se os gráficos com as demandas previstas para cada um deles."
      ],
      "metadata": {
        "id": "hcr9ZxSN4atv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# demanda AutoARIMA\n",
        "monthly_sales_pred_autoarima = (\n",
        "    Y_merged\n",
        "    .groupby(\"ds\")[\"AutoARIMA\"].sum()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "# demanda SeasonalNaive\n",
        "monthly_sales_pred_seasonalnaive = (\n",
        "    Y_merged\n",
        "    .groupby(\"ds\")[\"SeasonalNaive\"].sum()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "# dados reais\n",
        "monthly_sales_y = (\n",
        "    Y_merged\n",
        "    .groupby(\"ds\")[\"y\"].sum()\n",
        "    .reset_index()\n",
        ")"
      ],
      "metadata": {
        "id": "hZ7PhsuuCpB_"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# definindo o tamanho da imagem do gráfico\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "# Dados Reais\n",
        "df_real = monthly_sales_y.sort_values('ds', ascending=True)\n",
        "x_real = df_real['ds']\n",
        "y_real = df_real['y']\n",
        "plt.plot(x_real, y_real, label = 'Dados Reais')\n",
        "\n",
        "# Dados Previstos AutoARIMA\n",
        "df_pred_autoarima = monthly_sales_pred_autoarima.sort_values('ds', ascending=True)\n",
        "x_pred_autoarima = df_pred_autoarima['ds']\n",
        "y_pred_autoarima = df_pred_autoarima['AutoARIMA']\n",
        "plt.plot(x_pred_autoarima, y_pred_autoarima, color = 'orange', label = 'Dados Previstos AutoARIMA')\n",
        "\n",
        "# Dados Previstos SeasonalNaive\n",
        "df_pred_seasonalnaive = monthly_sales_pred_seasonalnaive.sort_values('ds', ascending=True)\n",
        "x_pred_seasonalnaive = df_pred_seasonalnaive['ds']\n",
        "y_pred_seasonalnaive = df_pred_seasonalnaive['SeasonalNaive']\n",
        "plt.plot(x_pred_seasonalnaive, y_pred_seasonalnaive, color = 'green', label = 'Dados Previstos SeasonalNaive')\n",
        "\n",
        "# Limite dos dados presentes na base de treino\n",
        "limit_date = pd.to_datetime('2014-03-01')\n",
        "plt.axvline(x = limit_date, color = 'red', linestyle = '--', label = 'Limite da base de treino')\n",
        "\n",
        "# denominando título, eixo x, eixo y, linhas de grade e legenda\n",
        "plt.title('Previsões de Demanda')\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Demanda')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle = '--', alpha=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "cNheGEIRCeT4",
        "outputId": "ff5a5f1e-7118-4e9d-e5c0-2ce824489519"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisando os resultados obtidos acima no gráfico, percebe-se que ambos os modelos treinados consegue captar razoavelmente bem as variações da demanda, considerando a faixa de dados da base de treino.\n",
        "\n",
        "Agora, quando entra-se na base de teste, percebe-se que o modelo de AutoARIMA capta um pouco melhor as variaçòes, contudo mantendo no geral uma tendência de crescimento. Já o modelo SeasonalNaive segue praticamente uma linear crescente de demanda. Ambos os modelos não conseguem captar de forma eficiente a queda brusca de demanda que ocorre a partir de 05/2014 dos dados reais."
      ],
      "metadata": {
        "id": "UxpbdgW79s89"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelos de Regressão"
      ],
      "metadata": {
        "id": "e5FVgHQoXDSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Avaliados os modelos de Série Temporal Hierárquica, agora será testado um modelo de regressão propriamente dito. Neste projeto, o modelo de regressão escolhido para ser testado foi o XGBoostRegressor, da biblioteca xgb, importada anteriormente.\n",
        "\n",
        "Assim, recordando-se a base de dados agrupada,"
      ],
      "metadata": {
        "id": "1v2tz92z-rB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R1xIdwqSD3hH",
        "outputId": "4b0eb20b-b5d6-4d05-af67-2ee434ae943e"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds       product        country          province  \\\n",
              "0 2011-05-01  AWC Logo Cap         Canada  British Columbia   \n",
              "1 2011-05-01  AWC Logo Cap         Canada           Ontario   \n",
              "2 2011-05-01  AWC Logo Cap         Canada            Quebec   \n",
              "3 2011-05-01  AWC Logo Cap  United States        California   \n",
              "4 2011-05-01  AWC Logo Cap  United States        California   \n",
              "\n",
              "                        store_name  y  \n",
              "0                      Retail Mall  4  \n",
              "1  Original Bicycle Supply Company  4  \n",
              "2          Superb Sales and Repair  5  \n",
              "3            Area Bike Accessories  3  \n",
              "4       Separate Parts Corporation  3  "
            ],
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              "      <th>ds</th>\n",
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              "      <th>province</th>\n",
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              "      <th>0</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Retail Mall</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Ontario</td>\n",
              "      <td>Original Bicycle Supply Company</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Quebec</td>\n",
              "      <td>Superb Sales and Repair</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Area Bike Accessories</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Separate Parts Corporation</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      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-adaebb57-3737-49cf-bf88-d3e9a26fdc11 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-adaebb57-3737-49cf-bf88-d3e9a26fdc11');\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-3c1dfe91-6dda-4c31-abac-11649fd22fa0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c1dfe91-6dda-4c31-abac-11649fd22fa0')\"\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-3c1dfe91-6dda-4c31-abac-11649fd22fa0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hts",
              "summary": "{\n  \"name\": \"hts\",\n  \"rows\": 77722,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 266,\n        \"samples\": [\n          \"LL Touring Frame - Yellow, 44\",\n          \"Road-250 Black, 58\",\n          \"Front Derailleur\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Canada\",\n          \"United States\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Victoria\",\n          \"British Columbia\",\n          \"Nord\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 634,\n        \"samples\": [\n          \"Self-Contained Cycle Parts Company\",\n          \"Hobby Store\",\n          \"Real Sporting Goods\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          2,\n          48,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Criando uma coluna com as categorias agrupadas"
      ],
      "metadata": {
        "id": "--zbnrPxD7gA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para adicionar novas features ao modelo, criou-se uma coluna a mais no dataset, de forma a concatenar todas as hierarquias definidas no modelo de série temporal hierárquica ('product', 'country', 'province', 'store_name')."
      ],
      "metadata": {
        "id": "0U_HXeYn-_pa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def concat(row):\n",
        "    return '/'.join(row)"
      ],
      "metadata": {
        "id": "ffGICPuCEFBy"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hts['concatenated'] = hts[['product', 'country', 'province', 'store_name']].apply(concat, axis=1)\n",
        "hts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9DWOMydaEBpT",
        "outputId": "21fde3aa-ee9a-4d7c-e040-7419242307cf"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds       product        country          province  \\\n",
              "0 2011-05-01  AWC Logo Cap         Canada  British Columbia   \n",
              "1 2011-05-01  AWC Logo Cap         Canada           Ontario   \n",
              "2 2011-05-01  AWC Logo Cap         Canada            Quebec   \n",
              "3 2011-05-01  AWC Logo Cap  United States        California   \n",
              "4 2011-05-01  AWC Logo Cap  United States        California   \n",
              "\n",
              "                        store_name  y  \\\n",
              "0                      Retail Mall  4   \n",
              "1  Original Bicycle Supply Company  4   \n",
              "2          Superb Sales and Repair  5   \n",
              "3            Area Bike Accessories  3   \n",
              "4       Separate Parts Corporation  3   \n",
              "\n",
              "                                        concatenated  \n",
              "0   AWC Logo Cap/Canada/British Columbia/Retail Mall  \n",
              "1  AWC Logo Cap/Canada/Ontario/Original Bicycle S...  \n",
              "2  AWC Logo Cap/Canada/Quebec/Superb Sales and Re...  \n",
              "3  AWC Logo Cap/United States/California/Area Bik...  \n",
              "4  AWC Logo Cap/United States/California/Separate...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-654af922-cc65-4d87-a4b7-065a1cea9389\" 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>ds</th>\n",
              "      <th>product</th>\n",
              "      <th>country</th>\n",
              "      <th>province</th>\n",
              "      <th>store_name</th>\n",
              "      <th>y</th>\n",
              "      <th>concatenated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>British Columbia</td>\n",
              "      <td>Retail Mall</td>\n",
              "      <td>4</td>\n",
              "      <td>AWC Logo Cap/Canada/British Columbia/Retail Mall</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Ontario</td>\n",
              "      <td>Original Bicycle Supply Company</td>\n",
              "      <td>4</td>\n",
              "      <td>AWC Logo Cap/Canada/Ontario/Original Bicycle S...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Quebec</td>\n",
              "      <td>Superb Sales and Repair</td>\n",
              "      <td>5</td>\n",
              "      <td>AWC Logo Cap/Canada/Quebec/Superb Sales and Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Area Bike Accessories</td>\n",
              "      <td>3</td>\n",
              "      <td>AWC Logo Cap/United States/California/Area Bik...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>AWC Logo Cap</td>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Separate Parts Corporation</td>\n",
              "      <td>3</td>\n",
              "      <td>AWC Logo Cap/United States/California/Separate...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-654af922-cc65-4d87-a4b7-065a1cea9389')\"\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",
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              "  </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-654af922-cc65-4d87-a4b7-065a1cea9389 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-654af922-cc65-4d87-a4b7-065a1cea9389');\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-04649268-4121-443b-865a-f8312c23c5a9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04649268-4121-443b-865a-f8312c23c5a9')\"\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-04649268-4121-443b-865a-f8312c23c5a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hts",
              "summary": "{\n  \"name\": \"hts\",\n  \"rows\": 77722,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 266,\n        \"samples\": [\n          \"LL Touring Frame - Yellow, 44\",\n          \"Road-250 Black, 58\",\n          \"Front Derailleur\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Canada\",\n          \"United States\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Victoria\",\n          \"British Columbia\",\n          \"Nord\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 634,\n        \"samples\": [\n          \"Self-Contained Cycle Parts Company\",\n          \"Hobby Store\",\n          \"Real Sporting Goods\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          2,\n          48,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"concatenated\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23605,\n        \"samples\": [\n          \"Sport-100 Helmet, Red/Australia/New South Wales/Bike Part Wholesalers\",\n          \"Road-250 Red, 44/United States/Texas/Fitness Toy Store\",\n          \"Half-Finger Gloves, S/Australia/Victoria/Fitness Discount Store\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definindo a coluna de datas mensais 'ds' como índice do data frame de regressão,"
      ],
      "metadata": {
        "id": "mBOtOe1aEVaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hts = hts.set_index('ds')"
      ],
      "metadata": {
        "id": "zsaevi6dEaoM"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, foram criados dois novos data frames, um sem a coluna dos dados concatenados, pois eles serão utilizados posteriormente ao treinamento do modelo de regressão. O outro se trata somente da coluna de dados concatenados, conforme segue,"
      ],
      "metadata": {
        "id": "QCY9bU7RBFUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_input_reg = hts.drop('concatenated', axis=1)\n",
        "df_concateneted = hts.loc[:, ['concatenated']]"
      ],
      "metadata": {
        "id": "bQFD1-nzElQ9"
      },
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tratando os valores categóricos do data frame"
      ],
      "metadata": {
        "id": "153QGtamEvFH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para rodar-se modelos de regressão, todas as features que apresentam dados categóricos precisam ser tratados de tal forma que estes dados devem ser substituídos por valores numéricos. Isto é necessário pois, modelos de regressão não conseguem tratar de forma adequada features categóricas.\n",
        "\n",
        "Desta forma, o pré-processador definido para ser utilizado no projeto foi o Ordinal Encoder, da biblioteca Scikitlearn:\n",
        "\n",
        "- OrdinalEncoder: usado nas features categóricas, este pré-processador basicamente irá substituir as diferentes entradas destas colunas por um número aletório. Diferentemente de outros pré processadores categóricos, como OneHotEncoder por exemplo, não serão criadas nenhuma nova coluna, apenas substituirá as entradas do tipo texto por números."
      ],
      "metadata": {
        "id": "O6NRNQ_qB3GT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# definindo o objeto encoder\n",
        "encoder = OrdinalEncoder()"
      ],
      "metadata": {
        "id": "MNNeb53kFFp8"
      },
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tratando as colunas categóricas\n",
        "model_input_reg['product'] = encoder.fit_transform(model_input_reg[['product']])\n",
        "model_input_reg['country'] = encoder.fit_transform(model_input_reg[['country']])\n",
        "model_input_reg['province'] = encoder.fit_transform(model_input_reg[['province']])\n",
        "model_input_reg['store_name'] = encoder.fit_transform(model_input_reg[['store_name']])"
      ],
      "metadata": {
        "id": "DAGJd-pMJShY"
      },
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizando os resultados do tratamento\n",
        "model_input_reg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ZR0dQsVEF5OO",
        "outputId": "96dca2a5-6017-4d71-ed04-be4d50c7f7ac"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product  country  province  store_name  y\n",
              "ds                                                   \n",
              "2011-05-01      0.0      1.0       5.0       446.0  4\n",
              "2011-05-01      0.0      1.0      44.0       368.0  4\n",
              "2011-05-01      0.0      1.0      47.0       530.0  5\n",
              "2011-05-01      0.0      5.0       7.0        22.0  3\n",
              "2011-05-01      0.0      5.0       7.0       491.0  3\n",
              "...             ...      ...       ...         ... ..\n",
              "2014-06-01    262.0      3.0      16.0       363.0  1\n",
              "2014-06-01    262.0      4.0      11.0       363.0  1\n",
              "2014-06-01    262.0      5.0       7.0       363.0  7\n",
              "2014-06-01    262.0      5.0      45.0       363.0  1\n",
              "2014-06-01    262.0      5.0      66.0       363.0  1\n",
              "\n",
              "[77722 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7922fd99-6351-4fbc-bebc-a42ef790fbc8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>2011-05-01</th>\n",
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              "    <tr>\n",
              "      <th>2011-05-01</th>\n",
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              "      <td>368.0</td>\n",
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              "    <tr>\n",
              "      <th>2011-05-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-05-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>22.0</td>\n",
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              "      <th>2011-05-01</th>\n",
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              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
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              "      <td>16.0</td>\n",
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              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
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              "    <tr>\n",
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              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77722 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-7922fd99-6351-4fbc-bebc-a42ef790fbc8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </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",
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              "    .colab-df-buttons div {\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7922fd99-6351-4fbc-bebc-a42ef790fbc8 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-7922fd99-6351-4fbc-bebc-a42ef790fbc8');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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-d6c31229-bb05-4a9f-a5c1-0b87988d28f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_473de1f7-28b2-4c32-b23e-d4307ed45256\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('model_input_reg')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_473de1f7-28b2-4c32-b23e-d4307ed45256 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('model_input_reg');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "model_input_reg",
              "summary": "{\n  \"name\": \"model_input_reg\",\n  \"rows\": 77722,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-05-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"2014-02-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.01485817836905,\n        \"min\": 0.0,\n        \"max\": 265.0,\n        \"num_unique_values\": 266,\n        \"samples\": [\n          97.0,\n          192.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8491682434519041,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          5.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.766892795012122,\n        \"min\": 0.0,\n        \"max\": 69.0,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          64.0,\n          5.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.05179313596335,\n        \"min\": 0.0,\n        \"max\": 633.0,\n        \"num_unique_values\": 634,\n        \"samples\": [\n          487.0,\n          255.0,\n          425.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          2,\n          48,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Divisão dos dados em treinamento e teste"
      ],
      "metadata": {
        "id": "cTdFYTdmF_UO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feito o tratamento, foram divididas as bases de treino e teste do modelo de regressão. Estas bases foram divididas da mesma forma que para os modelos de séries temporais hierárquicas, ou seja, os últimos 3 meses serão para teste e o restante para treino."
      ],
      "metadata": {
        "id": "65eo8hcrKEPI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# separando as bases\n",
        "train = model_input_reg.loc[model_input_reg.index < '2014-04-01']\n",
        "test = model_input_reg.loc[model_input_reg.index >= '2014-04-01']\n",
        "\n",
        "# definindo a variável \"alvo\" a ser prevista, ou seja, a demanda\n",
        "target = 'y'\n",
        "\n",
        "# definindo as colunas de features de treino e target de treino\n",
        "X_train = train.drop(columns = target)\n",
        "y_train = train[target]\n",
        "\n",
        "# definindo as colunas de features de teste e target de teste\n",
        "X_test = test.drop(columns = target)\n",
        "y_test = test[target]"
      ],
      "metadata": {
        "id": "DnPXhEZQF--y"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Treinamento do Modelo"
      ],
      "metadata": {
        "id": "xwAW7LqyGYAo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separadas as bases de treino e teste, o modelo foi definido e treinado, conforme segue abaixo."
      ],
      "metadata": {
        "id": "TZUZmJ4VK0rJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# definindo o modelo\n",
        "reg = xgb.XGBRegressor(base_score = 0.5, max_depth = 3, n_estimators = 1000, learning_rate = 0.01)"
      ],
      "metadata": {
        "id": "vvJ_2yQ4Gaus"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# treinando o modelo\n",
        "reg.fit(X_train, y_train, eval_set = [(X_train, y_train), (X_test, y_test)], verbose = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "wgMlyt97LGYF",
        "outputId": "a83d18b5-030f-41eb-997f-ab5413663af5"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-rmse:4.82470\tvalidation_1-rmse:6.54173\n",
            "[100]\tvalidation_0-rmse:3.83472\tvalidation_1-rmse:5.66577\n",
            "[200]\tvalidation_0-rmse:3.63474\tvalidation_1-rmse:5.41453\n",
            "[300]\tvalidation_0-rmse:3.56677\tvalidation_1-rmse:5.29631\n",
            "[400]\tvalidation_0-rmse:3.53535\tvalidation_1-rmse:5.23719\n",
            "[500]\tvalidation_0-rmse:3.50592\tvalidation_1-rmse:5.18025\n",
            "[600]\tvalidation_0-rmse:3.48577\tvalidation_1-rmse:5.14532\n",
            "[700]\tvalidation_0-rmse:3.46734\tvalidation_1-rmse:5.11550\n",
            "[800]\tvalidation_0-rmse:3.45168\tvalidation_1-rmse:5.08952\n",
            "[900]\tvalidation_0-rmse:3.43642\tvalidation_1-rmse:5.06273\n",
            "[999]\tvalidation_0-rmse:3.42316\tvalidation_1-rmse:5.03971\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com o modelo treinado, foram realizadas as previsões para os dados de teste. Estas previsões foram salvas em uma nova coluna 'prediction' dentro da base de teste definida anteriormente."
      ],
      "metadata": {
        "id": "O_RGot7-Lhrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test['prediction'] = reg.predict(X_test)"
      ],
      "metadata": {
        "id": "tZ5KKeqZHJGk"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Assim, a base de teste fica da seguinte forma,"
      ],
      "metadata": {
        "id": "xTtv_8LoNhH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "6XKrVgQoNkBu",
        "outputId": "5b228268-38ee-4bd7-be10-a856a29a58a9"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product  country  province  store_name   y  prediction\n",
              "ds                                                                \n",
              "2014-04-01      0.0      0.0      38.0       363.0  17    4.869244\n",
              "2014-04-01      0.0      0.0      48.0       363.0   5    4.259714\n",
              "2014-04-01      0.0      0.0      55.0       363.0   4    3.420081\n",
              "2014-04-01      0.0      0.0      58.0       363.0   1    3.420081\n",
              "2014-04-01      0.0      0.0      64.0       363.0   9    4.275659\n",
              "...             ...      ...       ...         ...  ..         ...\n",
              "2014-06-01    262.0      3.0      16.0       363.0   1    5.085087\n",
              "2014-06-01    262.0      4.0      11.0       363.0   1    6.747622\n",
              "2014-06-01    262.0      5.0       7.0       363.0   7    8.229268\n",
              "2014-06-01    262.0      5.0      45.0       363.0   1    6.003088\n",
              "2014-06-01    262.0      5.0      66.0       363.0   1    6.489276\n",
              "\n",
              "[5979 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3d79e18-4560-445c-b149-f6b97586759d\" 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>product</th>\n",
              "      <th>country</th>\n",
              "      <th>province</th>\n",
              "      <th>store_name</th>\n",
              "      <th>y</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>17</td>\n",
              "      <td>4.869244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>5</td>\n",
              "      <td>4.259714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.420081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.420081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>9</td>\n",
              "      <td>4.275659</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.085087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.747622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>7</td>\n",
              "      <td>8.229268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.003088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-06-01</th>\n",
              "      <td>262.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.489276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5979 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            style=\"display:none;\">\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        document.querySelector('#df-f3d79e18-4560-445c-b149-f6b97586759d 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-f3d79e18-4560-445c-b149-f6b97586759d');\n",
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              "                                                    [key], {});\n",
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              "\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "</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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "\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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              "    20% {\n",
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              "    40% {\n",
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              "      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-bc876cda-4f16-48cd-a2ba-036705ca9b32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1e9928d1-491a-4196-978a-74835730f6b0\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1e9928d1-491a-4196-978a-74835730f6b0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 5979,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-04-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2014-04-01 00:00:00\",\n          \"2014-05-01 00:00:00\",\n          \"2014-06-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84.6739059157085,\n        \"min\": 0.0,\n        \"max\": 265.0,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          257.0,\n          190.0,\n          191.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9260970997721318,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          1.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.48970134553832,\n        \"min\": 0.0,\n        \"max\": 69.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          33.0,\n          28.0,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129.57453678626675,\n        \"min\": 4.0,\n        \"max\": 629.0,\n        \"num_unique_values\": 180,\n        \"samples\": [\n          7.0,\n          477.0,\n          537.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          17,\n          24,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 1751,\n        \"samples\": [\n          3.8967487812042236,\n          1.738507628440857,\n          1.7616584300994873\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adicionando a coluna 'prediction' no Data Frame de input do modelo,"
      ],
      "metadata": {
        "id": "RNgAS9c-SEtN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_input_reg['prediction'] = pd.Series([float('NaN')] * len(model_input_reg), index  =model_input_reg.index)"
      ],
      "metadata": {
        "id": "lW9bATnKMvH1"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, adicionando todos os valores previstos nas últimas linhas da coluna 'prediction' criada anteriormente,"
      ],
      "metadata": {
        "id": "TBL0XBIyIOqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qty_predictions = len(test['prediction'].values)\n",
        "qty_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lys9s6YgmLoY",
        "outputId": "addb7154-f838-48d9-9ece-0552a575144b"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5979"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_input_reg.iloc[-qty_predictions:, model_input_reg.columns.get_loc('prediction')] = test['prediction'].values\n",
        "model_input_reg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "zn3fXZ50H_7R",
        "outputId": "2d75ef46-3e69-41cb-8cd1-f4129f3a42d4"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product  country  province  store_name  y  prediction\n",
              "ds                                                               \n",
              "2011-05-01      0.0      1.0       5.0       446.0  4         NaN\n",
              "2011-05-01      0.0      1.0      44.0       368.0  4         NaN\n",
              "2011-05-01      0.0      1.0      47.0       530.0  5         NaN\n",
              "2011-05-01      0.0      5.0       7.0        22.0  3         NaN\n",
              "2011-05-01      0.0      5.0       7.0       491.0  3         NaN\n",
              "...             ...      ...       ...         ... ..         ...\n",
              "2014-06-01    262.0      3.0      16.0       363.0  1    5.085087\n",
              "2014-06-01    262.0      4.0      11.0       363.0  1    6.747622\n",
              "2014-06-01    262.0      5.0       7.0       363.0  7    8.229268\n",
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            }
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Métricas do modelo"
      ],
      "metadata": {
        "id": "OL9al1irIbIk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com as previsões de teste realizadas, calculou-se as métricas do modelo de regressão."
      ],
      "metadata": {
        "id": "4BcRlq0jN0ec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = custom_rmse(test['y'], test['prediction'])\n",
        "mse = custom_mse(test['y'], test['prediction'])\n",
        "mae = custom_mae(test['y'], test['prediction'])\n",
        "\n",
        "results_xgboost = {'RMSE': [rmse], 'MSE': [mse], 'MAE': [mae]}\n",
        "results_df_xgboost = pd.DataFrame(results_xgboost)\n",
        "results_df_xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "DIWlNspWIh-j",
        "outputId": "98fa29af-4945-4a1d-935b-b5333f12ce8b"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RMSE   MSE   MAE\n",
              "0  5.04  25.4  2.53"
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              "type": "dataframe",
              "variable_name": "results_df_xgboost",
              "summary": "{\n  \"name\": \"results_df_xgboost\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.04,\n        \"max\": 5.04,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 25.4,\n        \"max\": 25.4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          25.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2.53,\n        \"max\": 2.53,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analisando a previsão da demanda dos próximos 3 meses utilizando o modelo regressivo"
      ],
      "metadata": {
        "id": "8MLbXJvLJJ9G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando as últimas 10 linhas com os resultados previstos pelo modelo,"
      ],
      "metadata": {
        "id": "fAsmN-omQ1MC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_input_reg.tail(10)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "ZsD82AB2Q6mW",
        "outputId": "dcce8cfd-3317-412b-bb3d-91a8b040fbaa"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product  country  province  store_name  y  prediction\n",
              "ds                                                               \n",
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              "      --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-a1164f9f-4f61-433f-ae47-b785956cdded button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"model_input_reg\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-06-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2014-06-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4216370213557839,\n        \"min\": 261.0,\n        \"max\": 262.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          262.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.90029237516523,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.91429754623281,\n        \"min\": 5.0,\n        \"max\": 66.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 363.0,\n        \"max\": 363.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7282079178254421,\n        \"min\": 3.1746933460235596,\n        \"max\": 8.229268074035645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.003088474273682\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mostrando no gráfico as previsões,"
      ],
      "metadata": {
        "id": "ITECylYEKBw_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# definindo o tamanho de figura\n",
        "fig, ax = plt.subplots(figsize = (12, 6))\n",
        "\n",
        "# dados reais\n",
        "plt.plot(model_input_reg[['y']], label = 'Dados Reais')\n",
        "\n",
        "# dados previstos pelo modelo de regressão\n",
        "plt.plot(model_input_reg['prediction'], color = 'orange', label = 'Dados Previstos')\n",
        "\n",
        "# limite dos dados presentes na base de treino\n",
        "limit_date = pd.to_datetime('2014-04-01')\n",
        "plt.axvline(x = limit_date, color = 'red', linestyle = '--', label = 'Limite da base de treino')\n",
        "\n",
        "# definindo o título do gráfico, eixo x, eixo y, linhas de grade e legenda\n",
        "plt.title('Previsões de Demanda')\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Previsão de Demanda')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle = '--', alpha=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "hUP9uWVMKErh",
        "outputId": "3708b369-9e30-41ca-8201-db78bde49994"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pelos resultados acima, percebe-se que o modelo de regressão conseguiu captar algumas variações, contudo as oscilações mais bruscas de demanda, a performance não foi tão satisfatório. Uma abordagem que poderia melhorar as performances deste modelo seria buscar otimizações dos seus hiperparâmetros, alinhada a um método de avaliação mais robusto, como a validação cruzada.\n",
        "\n",
        "Desta forma, poderia-se estudar mais afundo os principais comportamentos deste modelo e entender qual a combinação de hiperparâmetro apresenta o melhor resultado."
      ],
      "metadata": {
        "id": "vB2xR5LPmaIL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparando os resultados das métricas do modelo XGBoost com os modelos de série temporal hierárquica"
      ],
      "metadata": {
        "id": "wUIhtOrDOkog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# métricas do modelo de regressão\n",
        "results_df_xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "4dPpWNWGOnxM",
        "outputId": "1b514c98-701d-4485-e8ad-eff53e27a2e7"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RMSE   MSE   MAE\n",
              "0  5.04  25.4  2.53"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63cc8470-71b8-4b69-a9d9-49e760441f5e\" 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>RMSE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>MAE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.04</td>\n",
              "      <td>25.4</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63cc8470-71b8-4b69-a9d9-49e760441f5e')\"\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-63cc8470-71b8-4b69-a9d9-49e760441f5e 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-63cc8470-71b8-4b69-a9d9-49e760441f5e');\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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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df_xgboost')\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df_xgboost');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df_xgboost",
              "summary": "{\n  \"name\": \"results_df_xgboost\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.04,\n        \"max\": 5.04,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 25.4,\n        \"max\": 25.4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          25.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2.53,\n        \"max\": 2.53,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# métricas do modelo de série temporal hierárquica AutoARIMA\n",
        "results_auto_arima"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4jFPVaadOtT6",
        "outputId": "aa3036a3-7e62-4f78-9027-1a5c14a46ae0"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Category     RMSE         MSE      MAE\n",
              "0                              country  2041.05  4165876.13  1456.28\n",
              "1                             province   295.90    87554.26   156.72\n",
              "2                     country/province   295.90    87554.26   156.72\n",
              "3          country/province/store_name    90.69     8224.38    46.34\n",
              "4  country/province/store_name/product     4.19       17.60     2.77"
            ],
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              "      <td>87554.26</td>\n",
              "      <td>156.72</td>\n",
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              "      <td>country/province</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>country/province/store_name</td>\n",
              "      <td>90.69</td>\n",
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              "      <td>46.34</td>\n",
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              "      <th>4</th>\n",
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              "      <td>4.19</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_auto_arima')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_auto_arima');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_auto_arima",
              "summary": "{\n  \"name\": \"results_auto_arima\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"province\",\n          \"country/province/store_name/product\",\n          \"country/province\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 845.7450508457025,\n        \"min\": 4.19,\n        \"max\": 2041.05,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          295.9,\n          4.19,\n          2041.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1843011.707254459,\n        \"min\": 17.6,\n        \"max\": 4165876.13,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          87554.26,\n          17.6,\n          4165876.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614.4917228734656,\n        \"min\": 2.77,\n        \"max\": 1456.28,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          156.72,\n          2.77,\n          1456.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# métricas do modelo de série temporal hierárquica SeasonalNaive\n",
        "results_seasonal_naive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LDEJDAdoO1fS",
        "outputId": "1c3bf9b3-d986-4eb1-f7c3-6510838bdbd5"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Category     RMSE         MSE      MAE\n",
              "0                              country  2160.45  4667523.61  1389.61\n",
              "1                             province   284.28    80812.83   159.04\n",
              "2                     country/province   284.28    80812.83   159.04\n",
              "3          country/province/store_name   138.14    19083.96    63.23\n",
              "4  country/province/store_name/product     5.31       28.24     2.61"
            ],
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              "      <td>284.28</td>\n",
              "      <td>80812.83</td>\n",
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              "\n",
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              "\n",
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              "      }\n",
              "    </style>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_seasonal_naive",
              "summary": "{\n  \"name\": \"results_seasonal_naive\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"province\",\n          \"country/province/store_name/product\",\n          \"country/province\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 894.1588232355591,\n        \"min\": 5.31,\n        \"max\": 2160.45,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          284.28,\n          5.31,\n          2160.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2067490.8959244608,\n        \"min\": 28.24,\n        \"max\": 4667523.61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          80812.83,\n          28.24,\n          4667523.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 582.3500897484261,\n        \"min\": 2.61,\n        \"max\": 1389.61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          159.04,\n          2.61,\n          1389.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisando-se os resultados acima, se pegarmos como base o nível mais granular das hierarquias dos modelos de série temporal hierárquica, percebe-se que os 3 modelos testados apresentaram métricas muito parecidas, com poucas variações entre elas.\n",
        "\n",
        "Contudo, avaliando-se as métricas dos modelos de série temporal de forma agregada, considerando todas as hierarquias,"
      ],
      "metadata": {
        "id": "5yK_mJeLnSLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Métricas agregadas modelo AutoARIMA\n",
        "rmse = custom_rmse(y = Y_merged['y'], y_pred = Y_merged['AutoARIMA'])\n",
        "mse = custom_mse(y = Y_merged['y'], y_pred = Y_merged['AutoARIMA'])\n",
        "mae = custom_mae(y = Y_merged['y'], y_pred = Y_merged['AutoARIMA'])\n",
        "\n",
        "print(f'RMSE: {rmse:.2f}, MSE: {mse:.2f}, MAE: {mae:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp9euqJqoF6b",
        "outputId": "363b93d9-1fcc-4d88-a06c-1f77d6ff0ff0"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 143.42, MSE: 20568.20, MAE: 20.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Métricas agregadas modelo SeasonalNaive\n",
        "rmse = custom_rmse(y = Y_merged['y'], y_pred = Y_merged['SeasonalNaive'])\n",
        "mse = custom_mse(y = Y_merged['y'], y_pred = Y_merged['SeasonalNaive'])\n",
        "mae = custom_mae(y = Y_merged['y'], y_pred = Y_merged['SeasonalNaive'])\n",
        "\n",
        "print(f'RMSE: {rmse:.2f}, MSE: {mse:.2f}, MAE: {mae:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EDUrtUap2V7",
        "outputId": "778275be-4e1d-49af-f866-a087ddda8bfd"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 150.67, MSE: 22702.27, MAE: 21.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Percebe-se que as métricas de ambos os modelos de série temporal hierárquica tiveram RMSE perto dos 150, MSE maior que 20000 e MAE perto de 20. Já o modelo XGBoost teve RMSE = 5.04, MSE = 25.4 e MAE = 2.53.\n",
        "\n",
        "Desta forma, avaliando no todo, o modelo de regressão apresentou melhores métricas e portanto é o escolhido para avaliar as demandas a seguir por centro de Distribuição."
      ],
      "metadata": {
        "id": "ZMUi-RDiqAy4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análises dos Centros de Distribuição"
      ],
      "metadata": {
        "id": "5ct2B71jLMak"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adicionando a coluna com as categorias agregadas no data frame completo com as previsões."
      ],
      "metadata": {
        "id": "K0RB6s4QRdmh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_input_reg['product/country/province/store_name'] = df_concateneted['concatenated']"
      ],
      "metadata": {
        "id": "1W1AJGLXRZ7U"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filtrando o dataset, selecionando apenas os dados entre 01-01-2024 e 30-06-2014"
      ],
      "metadata": {
        "id": "UprIv6ejTGz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# definindo as datas de interesse\n",
        "start_date= '2014-01-01'\n",
        "final_date = '2014-06-30'"
      ],
      "metadata": {
        "id": "E6h-WvybT05q"
      },
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered = model_input_reg.loc[start_date:final_date]\n",
        "df_filtered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "j2rZ2-qLLROn",
        "outputId": "625b3b66-20ff-4035-c703-30a0b0617feb"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product  country  province  store_name   y  prediction  \\\n",
              "ds                                                                   \n",
              "2014-01-01      0.0      0.0      38.0        46.0   4         NaN   \n",
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              "2014-01-01      0.0      0.0      48.0       363.0  10         NaN   \n",
              "...             ...      ...       ...         ...  ..         ...   \n",
              "2014-06-01    262.0      3.0      16.0       363.0   1    5.085087   \n",
              "2014-06-01    262.0      4.0      11.0       363.0   1    6.747622   \n",
              "2014-06-01    262.0      5.0       7.0       363.0   7    8.229268   \n",
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              "\n",
              "                          product/country/province/store_name  \n",
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              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Popular...  \n",
              "2014-01-01      AWC Logo Cap/Australia/Queensland/Online Sell  \n",
              "...                                                       ...  \n",
              "2014-06-01  Women's Mountain Shorts, S/Germany/Hamburg/Onl...  \n",
              "2014-06-01  Women's Mountain Shorts, S/United Kingdom/Engl...  \n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Calif...  \n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Orego...  \n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Washi...  \n",
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              "type": "dataframe",
              "variable_name": "df_filtered",
              "summary": "{\n  \"name\": \"df_filtered\",\n  \"rows\": 16235,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2014-01-01 00:00:00\",\n          \"2014-02-01 00:00:00\",\n          \"2014-06-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84.18192522501398,\n        \"min\": 0.0,\n        \"max\": 265.0,\n        \"num_unique_values\": 180,\n        \"samples\": [\n          28.0,\n          79.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8903559722581813,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          1.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"province\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.550675189486977,\n        \"min\": 0.0,\n        \"max\": 69.0,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          41.0,\n          38.0,\n          42.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141.2539751093831,\n        \"min\": 4.0,\n        \"max\": 630.0,\n        \"num_unique_values\": 456,\n        \"samples\": [\n          299.0,\n          568.0,\n          530.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 67,\n        \"samples\": [\n          56,\n          11,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4501549313518514,\n        \"min\": 0.7952322363853455,\n        \"max\": 13.119024276733398,\n        \"num_unique_values\": 1751,\n        \"samples\": [\n          3.8967487812042236,\n          1.738507628440857,\n          1.7616584300994873\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product/country/province/store_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10202,\n        \"samples\": [\n          \"LL Mountain Pedal/United Kingdom/England/Commerce Bicycle Specialists\",\n          \"Touring-1000 Yellow, 60/France/Hauts de Seine/Roadway Bicycle Supply\",\n          \"ML Road Pedal/Canada/Ontario/Our Sporting Goods Store\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecionando apenas as colunas de interesse para analisar as distribuições,"
      ],
      "metadata": {
        "id": "PcNQhFcOTnRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = df_filtered[['product/country/province/store_name', 'y', 'prediction']]\n",
        "final_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "r3I2hlKbLZV2",
        "outputId": "0fea2e5e-9765-4cdd-d43a-f15eef6b33b4"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          product/country/province/store_name   y  prediction\n",
              "ds                                                                           \n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Bike Pa...   4         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Nationw...  16         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Online ...  27         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Popular...   7         NaN\n",
              "2014-01-01      AWC Logo Cap/Australia/Queensland/Online Sell  10         NaN\n",
              "...                                                       ...  ..         ...\n",
              "2014-06-01  Women's Mountain Shorts, S/Germany/Hamburg/Onl...   1    5.085087\n",
              "2014-06-01  Women's Mountain Shorts, S/United Kingdom/Engl...   1    6.747622\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Calif...   7    8.229268\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Orego...   1    6.003088\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Washi...   1    6.489276\n",
              "\n",
              "[16235 rows x 3 columns]"
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              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
              "      <td>AWC Logo Cap/Australia/New South Wales/Bike Pa...</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2014-01-01</th>\n",
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              "      <td>AWC Logo Cap/Australia/Queensland/Online Sell</td>\n",
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              "      <th>2014-06-01</th>\n",
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              "      <th>2014-06-01</th>\n",
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              "      <th>2014-06-01</th>\n",
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              "      <th>2014-06-01</th>\n",
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              "    async function quickchart(key) {\n",
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              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 16235,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-01 00:00:00\",\n        \"max\": \"2014-06-01 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2014-01-01 00:00:00\",\n          \"2014-02-01 00:00:00\",\n          \"2014-06-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product/country/province/store_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10202,\n        \"samples\": [\n          \"LL Mountain Pedal/United Kingdom/England/Commerce Bicycle Specialists\",\n          \"Touring-1000 Yellow, 60/France/Hauts de Seine/Roadway Bicycle Supply\",\n          \"ML Road Pedal/Canada/Ontario/Our Sporting Goods Store\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 67,\n        \"samples\": [\n          56,\n          11,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4501549313518514,\n        \"min\": 0.7952322363853455,\n        \"max\": 13.119024276733398,\n        \"num_unique_values\": 1751,\n        \"samples\": [\n          3.8967487812042236,\n          1.738507628440857,\n          1.7616584300994873\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Selecionando os registros dos EUA e dos outros países"
      ],
      "metadata": {
        "id": "fO4Afz9mL0Lf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dados dos EUA\n",
        "df_usa = final_df[final_df['product/country/province/store_name'].str.contains('United States')]\n",
        "\n",
        "# dados de qualquer outro país que não seja os EUA\n",
        "df_outside_usa = final_df[~final_df['product/country/province/store_name'].str.contains('United States')]"
      ],
      "metadata": {
        "id": "dI_bRcFYL5l1"
      },
      "execution_count": 242,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para o cálculo de crescimento da demanda, foi definida a seguinte função abaixo, no qual ela irá receber os dados entre 01/01/2024 e 03/01/2014 definidos como os dados da demanda dos 3 meses anterior a previsão. E os dados entre 01/04/2024 e 01/06/2024 foram os dados previstos de demanda para os 3 meses posteriores."
      ],
      "metadata": {
        "id": "aK5AKhtmUMDg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def demand(df:pd.DataFrame) -> dict:\n",
        "\n",
        "    first_df = df.loc['2014-01-01':'2014-03-01']\n",
        "    first_demand = first_df['y'].sum()\n",
        "\n",
        "    last_df = df.loc['2014-04-01':'2014-06-01']\n",
        "    prediction_demand = last_df['prediction'].sum()\n",
        "\n",
        "    results = prediction_demand - first_demand\n",
        "\n",
        "    return {'Demanda Jan a Março 2014': round(first_demand), 'Previsão de Demanda Abril a Junho 2014': round(prediction_demand), 'Crescimento': round(results)}"
      ],
      "metadata": {
        "id": "yMe_1wXaMHQ1"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Assim, calculando a demanda dos centros de distribuição presentes nos EUA,"
      ],
      "metadata": {
        "id": "WLBJSrbnMQ6d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "demand_usa = demand(df_usa)\n",
        "demand_usa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCA-NX0uMOpl",
        "outputId": "2cfb5d85-bf9f-43f4-cfff-e31108b680f2"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Demanda Jan a Março 2014': 18303,\n",
              " 'Previsão de Demanda Abril a Junho 2014': 8769,\n",
              " 'Crescimento': -9534}"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando agora a previsão de demandas dos centros de distribuição fora dos EUA,"
      ],
      "metadata": {
        "id": "_ldoSP76Mjp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "demand_outside_usa = demand(df_outside_usa)\n",
        "demand_outside_usa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fV2_UJV0MsSY",
        "outputId": "dee45042-bb60-40b9-c577-0222a182c049"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Demanda Jan a Março 2014': 20029,\n",
              " 'Previsão de Demanda Abril a Junho 2014': 13032,\n",
              " 'Crescimento': -6997}"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Portanto, observa-se uma queda na previsão de demanda para os próximos 3 meses tanto para os centros de distribuição dos EUA, quando para o restante do mundo. Contudo, observa-se que os países fora dos EUA tiveram uma demanda prevista agregada de 13032 produtos, enquanto que dentro dos EUA foi de 8769. Portanto, a demanda fora dos EUA prevista é maior."
      ],
      "metadata": {
        "id": "cfMHBh7-M9ro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Previsão da quantidade de Zíperes a serem produzidos"
      ],
      "metadata": {
        "id": "YhqRepXKN8aX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Um novo fornecedor de luvas, que agora engloba toda a produção mundial, precisa ter uma estimativa de quantos zíperes precisa pedir para os próximos 3 meses. Para responder isto, sabe-se que são necessários 2 zíperes por par de luva.\n",
        "\n",
        "Portano, para realizar tal cálculo, é necessário saber da previsão de demanda de luvas para os próximos 3 meses, assim tem-se também a demanda de zíperes.\n",
        "\n",
        "Logo, primeiro filtrou-se o Data Frame final, de tal forma que foram selecionadas todas as linhas da coluna agregada \"product/country/province/store\" que apresenta o termo \"Gloves\" em sua composição."
      ],
      "metadata": {
        "id": "BEQKmjHPXQ55"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# selecionando somente os índices das linhas que apresentam a palavra \"Gloves\" dentro da coluna concatenada \"product/country/province/store_name\"\n",
        "gloves_rows = final_df[final_df['product/country/province/store_name'].str.contains('Gloves')].index"
      ],
      "metadata": {
        "id": "RNVuo567NjwB"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# filtrando o data frame final\n",
        "df_gloves = final_df.loc[gloves_rows]\n",
        "df_gloves"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "3m-nUUElWyQ-",
        "outputId": "ad96fa75-e1c9-41ce-f7e5-fd4d10900e05"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          product/country/province/store_name   y  prediction\n",
              "ds                                                                           \n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Bike Pa...   4         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Nationw...  16         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Online ...  27         NaN\n",
              "2014-01-01  AWC Logo Cap/Australia/New South Wales/Popular...   7         NaN\n",
              "2014-01-01      AWC Logo Cap/Australia/Queensland/Online Sell  10         NaN\n",
              "...                                                       ...  ..         ...\n",
              "2014-06-01  Women's Mountain Shorts, S/Germany/Hamburg/Onl...   1    5.085087\n",
              "2014-06-01  Women's Mountain Shorts, S/United Kingdom/Engl...   1    6.747622\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Calif...   7    8.229268\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Orego...   1    6.003088\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Washi...   1    6.489276\n",
              "\n",
              "[1620981 rows x 3 columns]"
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_gloves"
            }
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecionando somente as datas previstas dos últimos 3 meses,"
      ],
      "metadata": {
        "id": "dOdcHRQFPj_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_gloves = df_gloves.loc['2014-04-01':'2014-06-30']\n",
        "df_gloves"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "CWftir-KPCz8",
        "outputId": "d570d165-b6f2-48f9-dfc7-ec3f341597c5"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          product/country/province/store_name   y  prediction\n",
              "ds                                                                           \n",
              "2014-04-01  AWC Logo Cap/Australia/New South Wales/Online ...  17    4.869244\n",
              "2014-04-01      AWC Logo Cap/Australia/Queensland/Online Sell   5    4.259714\n",
              "2014-04-01  AWC Logo Cap/Australia/South Australia/Online ...   4    3.420081\n",
              "2014-04-01        AWC Logo Cap/Australia/Tasmania/Online Sell   1    3.420081\n",
              "2014-04-01        AWC Logo Cap/Australia/Victoria/Online Sell   9    4.275659\n",
              "...                                                       ...  ..         ...\n",
              "2014-06-01  Women's Mountain Shorts, S/Germany/Hamburg/Onl...   1    5.085087\n",
              "2014-06-01  Women's Mountain Shorts, S/United Kingdom/Engl...   1    6.747622\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Calif...   7    8.229268\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Orego...   1    6.003088\n",
              "2014-06-01  Women's Mountain Shorts, S/United States/Washi...   1    6.489276\n",
              "\n",
              "[488565 rows x 3 columns]"
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              "      <th>2014-04-01</th>\n",
              "      <td>AWC Logo Cap/Australia/New South Wales/Online ...</td>\n",
              "      <td>17</td>\n",
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              "    <tr>\n",
              "      <th>2014-04-01</th>\n",
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              "      <th>2014-06-01</th>\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_gloves"
            }
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Assim, a quantidade total de pares de luvas será,"
      ],
      "metadata": {
        "id": "PYa1uDbNPs_q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gloves = df_gloves['prediction'].sum()\n",
        "pair_of_gloves = round(gloves/2)\n",
        "pair_of_gloves"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fs7TZ-nGP2l9",
        "outputId": "814d9a08-4666-4797-89be-b14b26b8030a"
      },
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "878107"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Portanto, existe uma demanda de 878107 pares de luvas. Como é necessário 2 zíperes por par, logo"
      ],
      "metadata": {
        "id": "6hMCyCnMXyBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ziper = pair_of_gloves*2\n",
        "ziper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1Hy6Dsyw_XB",
        "outputId": "5d2a134f-cada-418e-c26c-9bf565dee560"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1756214"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Desta forma, a demanda de zíperes é de 1756214."
      ],
      "metadata": {
        "id": "wdc_GSvmxOlB"
      }
    }
  ]
}