基于层次贝叶斯的改进模型，有效解决稀疏需求预测难题
Taxonomy-Conditioned Hierarchical Bayesian TSB Models for Heterogeneous Intermittent Demand Forecasting
- 用分层贝叶斯框架重构经典TSB方法，引入共用先验提升稳定性
- 在UCI在线零售数据上RMSE和RMSSE均低于所有基线模型
- 适合处理冷启动、稀疏或异质性需求场景，尤其适用于库存管理
间歇性需求预测因观测稀疏、新品冷启动和产品过时而面临挑战。传统方法如Croston、SBA及Teunter-Syntetos-Babai（TSB）虽具启发性，但缺乏严格的生成建模基础。本文提出TSB-HB，即对TSB的层次贝叶斯扩展：需求发生采用贝塔-二项分布，非零需求量服从对数正态分布。关键在于通过层次先验实现跨商品的部分信息共享，在保持异质性的同时稳定稀疏或冷启动序列的估计。该框架为经典TSB结构提供了统一的生成解释。在UCI Online Retail数据集上，TSB-HB的RMSE与RMSSE均优于所有基线，且在MAE上仍具竞争力；在5,000条M5样本中，其MAE与RMSE显著优于经典间歇性基线。在校准的概率设定下，其分位数损失表现良好，且在参数化模型中实现了更优的锐度-校准权衡。
原文摘要 · Abstract (English)
Intermittent demand forecasting poses unique challenges due to sparse observations, cold-start items, and obsolescence. Classical models such as Croston, SBA, and the Teunter--Syntetos--Babai (TSB) method provide simple heuristics but lack a principled generative foundation. We introduce TSB-HB, a hierarchical Bayesian extension of TSB. Demand occurrence is modeled with a Beta--Binomial distribution, while nonzero demand sizes follow a Log-Normal distribution. Crucially, hierarchical priors enable partial pooling across items, stabilizing estimates for sparse or cold-start series while preserving heterogeneity. This framework provides a coherent generative reinterpretation of the classical TSB structure. On the UCI Online Retail dataset, TSB-HB achieves the lowest RMSE and RMSSE among all baselines, while remaining competitive in MAE. On a 5,000-series M5 sample, it improves MAE and RMSE over classical intermittent baselines. Under the calibrated probabilistic configuration, TSB-HB yields competitive pinball loss and a favorable sharpness--calibration tradeoff among the parametric baselines reported in the main text.
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