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Forecasting Lumpy and Intermittent Stores Sales Series with Global Recursive Machine Learning Using Modeltime
This study aims to compare the performance of traditional time series models with machine learning models, especially XGBoost, for forecasting intermittent and lumpy demand. It also evaluates the effectiveness of global models compared to local approaches, considering the heterogeneity of the product base
