根据商品需求特点自动选模型，提升供应链预测精度
Intelligent Routing for Sparse Demand Forecasting: A Comparative Evaluation of Selection Strategies
- 用规则、LightGBM或InceptionTime动态选适配的预测模型
- 在Favorita数据集上准确率提升11.8%（NWRMSLE），推理快4.67倍
- 适合需要精准库存管理的零售与供应链场景
供应链中稀疏且间歇性需求预测面临严峻挑战，频繁的零需求期会降低传统模型精度并影响库存管理。我们提出并评估了一种模型路由框架（Model-Router），可根据每种商品的独特需求模式，动态选择最适合的预测模型——涵盖经典方法、机器学习和深度学习模型。通过对比基于规则、LightGBM和InceptionTime的路由策略，该方法能有效区分平稳、波动或间歇性需求模式，优化预测结果。在大规模Favorita数据集上的实验表明，采用深度学习（InceptionTime）的路由策略相比强基线单模型，预测准确率最高提升11.8%（NWRMSLE），推理速度加快4.67倍。这些预测精度的提升将显著减少缺货与过度库存，凸显智能自适应算法在现代供应链优化中的关键作用。
原文摘要 · Abstract (English)
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.
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