A Diversified Integrated Model for Seasonal Product Demand Prediction

Abstract Product demand forecasting is the core link of an intelligent supply chain. The article discusses the demand characteristics of seasonal fast-moving consumer goods and presents a diversified stacked regression model (RXOEL-X) that combines linear and multi-machine learning models. This model utilizes a model stacking strategy and adopts the ElasticNet model, combined with L1 and L2 regularization to handle complex relationships in the data and prevent overfitting. Empirical evaluation using real data from leading beverage companies demonstrates the model's superiority over other time series forecasting techniques in demand forecasting for smart supply chains.