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l train and evaluate multiple time-series forecasting models using the Store Item Demand Forecasting Challenge dataset from Kaggle. This dataset has 10 different stores and each store has 50 items, i.e. total of 500 daily level time series data for five years (2013–2017).
Este projeto utiliza dbt para o tratamento de dados, que são aplicados em business intelligence com dashboards, e em data science para previsão de demanda, utilizando séries temporais hierarquizadas com modelos ARIMA e regressão linear múltipla