TSB-HB: A Hierarchical Bayesian Extension of the TSB Model for Intermittent Demand Forecasting

• URL: http://arxiv.org/abs/2511.12749v1
• Date: Sun, 16 Nov 2025 19:24:33 GMT
• Title: TSB-HB: A Hierarchical Bayesian Extension of the TSB Model for Intermittent Demand Forecasting
• Abstract summary: Intermittent demand forecasting poses unique challenges due to sparse observations, cold-start items, and obsolescence.<n>Deep learning models address these limitations but often require large datasets and sacrifice interpretability.<n>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.<n>This framework yields a fully generative and interpretable model that generalizes classical exponential smoothing.
• Score: 0.8594140167290097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Deep learning models address these limitations but often require large datasets and sacrifice interpretability. 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 yields a fully generative and interpretable model that generalizes classical exponential smoothing. On the UCI Online Retail dataset, TSB-HB achieves lower RMSE and RMSSE than Croston, SBA, TSB, ADIDA, IMAPA, ARIMA and Theta, and on a subset of the M5 dataset it outperforms all classical baselines we evaluate. The model provides calibrated probabilistic forecasts and improved accuracy on intermittent and lumpy items by combining a generative formulation with hierarchical shrinkage, while remaining interpretable and scalable.

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