A Stacking-Based Fusion Framework for Dynamic Demand Forecasting in E-Commerce

In response to the growing complexity of e-commerce warehouse management driven by the expansion of live-streaming and cross-border businesses, this study tackles the critical challenge of product demand forecasting. We propose an intelligent forecasting approach based on a multi-model fusion framework, constructing a Stacking ensemble that integrates XGBoost, LightGBM, and CatBoost as base learners. Hyperparameter optimization is systematically conducted using grid search combined with cross-validation. To account for periodic trends, seasonal fluctuations are modeled as explicit temporal features, and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is incorporated to perform joint forecasting by capturing residual time-dependent patterns. Experimental results show that the proposed fusion model consistently outperforms all individual base learners across multiple metrics, including R2, RMSE, MAE, MAPE, Precision, Recall, and Accuracy. Furthermore, the mean cosine similarity of the forecast sequences reaches 0.986, underscoring both the stability of seasonal representations and the model’s robustness in capturing demand variations. This method effectively improves the accuracy of e-commerce product demand forecasts, offering reliable data support for inventory management and allocation strategies.