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  "fetched_at": "2026-10-20T07:42:38Z",
  "source_url": "https://www.kaggle.com/competitions/99dapt-sries-temporais-i/data",
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      "content": "A variável resposta é a variável **sales_per_trans**.\n\n## Files\n\n### **train.csv** \n\nO conjunto de treinamento. Contém as variáveis:\n* **id** - o ID da observação\n* **date** - data em format YYYY-MM-DD\n* **store_nbr** - o código de cada uma das duas lojas\n* **onpromotion** - o número de itens da categoria **GROCERIES I** promocionados na data/loja\n* **sales_per_trans** - nossa **variável resposta**, contém o número de itens vendidos por transação da loja\n\n### **test.csv** - o conjunto de teste\n\nO conjunto de teste. Contém as variáveis:\n* **id** - o ID da observação\n* **date** - data em format YYYY-MM-DD\n* **store_nbr** - o código de cada uma das duas lojas\n* **onpromotion** - o número de itens da categoria **GROCERIES I** promocionados na data/loja\n\n### **stores.csv** - tabela com localização e características das lojas\n\nInformações sobre as diferentes lojas da rede Favorita. Contém as variáveis:\n* **store_nbr** - o código de cada loja\n* **city** - a cidade da loja\n* **state** - o estado da loja\n* **type** - o tipo de loja (não precisa ser utilizado nesse problema)\n* **cluster** - o cluster da loja (não precisa ser utilizado nesse problema)\n\n### **holiday_events.csv** - tabela de feriados regionais do Equador\nHolidays and Events, with metadata\nNOTE: Pay special attention to the transferred column. A holiday that is transferred officially falls on that calendar day, but was moved to another date by the government. A transferred day is more like a normal day than a holiday. To find the day that it was actually celebrated, look for the corresponding row where type is Transfer. For example, the holiday Independencia de Guayaquil was transferred from 2012-10-09 to 2012-10-12, which means it was celebrated on 2012-10-12. Days that are type Bridge are extra days that are added to a holiday (e.g., to extend the break across a long weekend). These are frequently made up by the type Work Day which is a day not normally scheduled for work (e.g., Saturday) that is meant to payback the Bridge.\nAdditional holidays are days added a regular calendar holiday, for example, as typically happens around Christmas (making Christmas Eve a holiday).\n\n### **oil.csv** - tabela de preços do Petróleo para período de treinamento\n\nDaily oil price. Ecuador is an oil-dependent country and it's economical health is highly vulnerable to shocks in oil prices.\n\n### **sample_submission.csv** - um exemplo de arquivo de submissão.",
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      "name": "Description",
      "content": "O objetivo da competição é praticarmos o que aprendemos durante a semana.\n\nEsta competição foi estruturada a partir da competição [Store Sales - Time Series Forecasting](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview). Para conseguirmos desenvolver o projeto em apenas um dia, a competição inicial foi reduzida para apenas uma categoria e duas lojas (duas séries temporais).\n\nDiscutiremos os detalhes da competição em aula! Segue abaixo o texto da competição original:\n\n# Goal of the Competition\n\nIn this “getting started” competition, you’ll use time-series forecasting to forecast store sales on data from Corporación Favorita, a large Ecuadorian-based grocery retailer.\n\nSpecifically, you'll build a model that more accurately predicts the **sales per transaction** for 2 stores from Corporation Favorita\n\n# Get Started\n\nForecasts aren’t just for meteorologists. Governments forecast economic growth. Scientists attempt to predict the future population. And businesses forecast product demand—a common task of professional data scientists. Forecasts are especially relevant to brick-and-mortar grocery stores, which must dance delicately with how much inventory to buy. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leading to lost revenue and upset customers. More accurate forecasting, thanks to machine learning, could help ensure retailers please customers by having just enough of the right products at the right time.\n\nCurrent subjective forecasting methods for retail have little data to back them up and are unlikely to be automated. The problem becomes even more complex as retailers add new locations with unique needs, new products, ever-transitioning seasonal tastes, and unpredictable product marketing.\n\n# Potential Impact\n\nIf successful, you'll have flexed some new skills in a real world example. For grocery stores, more accurate forecasting can decrease food waste related to overstocking and improve customer satisfaction. The results of this ongoing competition, over time, might even ensure your local store has exactly what you need the next time you shop.\n\n# Créditos\n\nThis dataset is a subset/transformation of the Favorita stores competition: https://www.kaggle.com/competitions/store-sales-time-series-forecasting/data",
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    {
      "name": "Evaluation",
      "content": "A avaliação do erro sera sobre o período de 3 meses após o fim do período de treinamento. A medida de erro utilizada será o RMSE.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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