2025/06/04 by Qiwen Zhang, Zhang, Qiwen
Decision Sciences · Engineering · #68T20 #FOS: Computer and information sciences #Forecasting Techniques and Applications #I.2.0 #Machine Learning (cs.LG) #Stock Market Forecasting Methods #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2506.14810
openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sparse and intermittent demand forecasting in supply chains presents a critical challenge, as frequent zero-demand periods hinder traditional model accuracy and impact inventory management. We propose and evaluate a Model-Router framework that dynamically selects the most suitable forecasting model-spanning classical, ML, and DL methods for each product based on its unique demand pattern. By comparing rule-based, LightGBM, and InceptionTime routers, our approach learns to assign appropriate forecasting strategies, effectively differentiating between smooth, lumpy, or intermittent demand regimes to optimize predictions. Experiments on the large-scale Favorita dataset show our deep learning (Inception Time) router improves forecasting accuracy by up to 11.8% (NWRMSLE) over strong, single-model benchmarks with 4.67x faster inference time. Ultimately, these gains in forecasting precision will drive substantial reductions in both stockouts and wasteful excess inventory, underscoring the critical role of intelligent, adaptive Al in optimizing contemporary supply chain operations.