TSB-HB: A Hierarchical Bayesian Extension of the TSB Model for Intermittent Demand Forecasting
This addresses forecasting challenges for sparse, intermittent demand items in retail and supply chain contexts, representing an incremental improvement over existing methods.
The paper tackles intermittent demand forecasting by introducing TSB-HB, a hierarchical Bayesian extension of the TSB model, which achieves lower RMSE and RMSSE than classical baselines like Croston, SBA, and ARIMA on datasets such as UCI Online Retail and M5.
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.