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.

翻译：间歇性需求预测因观测稀疏、冷启动产品及过时问题而面临独特挑战。经典模型如Croston法、SBA法及Teunter-Syntetos-Babai（TSB）方法虽提供简单启发式规则，但缺乏严谨的生成式理论基础。我们提出TSB-HB——TSB的分层贝叶斯扩展框架。需求发生概率采用Beta-Binomial分布建模，非零需求规模则遵循Log-Normal分布。关键在于，分层先验机制实现了跨产品类别的部分信息汇集，在保持异质性的同时稳定了稀疏序列与冷启动序列的估计。该框架为经典TSB结构提供了自洽的生成式重解释。在UCI在线零售数据集上，TSB-HB在RMSE与RMSSE指标中取得最优表现，同时MAE指标保持竞争力。在包含5000个时间序列的M5样本中，TSB-HB相较于经典间歇性基线模型显著改善了MAE与RMSE。在校准概率配置下，TSB-HB在正文报告的参数化基线模型中展现出具有竞争力的分位数损失，并实现了有利的锐度-校准权衡。