Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons

Abstract:Forecasting-model selection remains difficult in heterogeneous demand because the most suitable decision rule may vary with demand structure, data availability, and forecasting horizon. This study examines whether the selector itself should be treated as a context-dependent component of the forecasting process. Five selection mechanisms - RMSSE, ERA, OWA, CCG-AHSC, and CCG-AHSCD - are compared across 24 optimized forecasting models, nine datasets, three training-testing partitions, and horizons from 1 to 12 cycles. Selector performance is evaluated ex post using Global Relative Accuracy (GRA), statistical tests, and a best-attainable-model reference. No selector dominates across all conditions. CCG-AHSC and CCG-AHSCD are more competitive for Smooth demand and several Erratic configurations, whereas OWA and ERA perform better in Intermittent and Lumpy settings. Selector suitability also changes with historical data availability and horizon, supporting a context-dependent rather than universal approach to forecasting-model selection.

Machine Learning (cs.LG)

arXiv:2609.04425 [cs.LG]

(or arXiv:2609.04425v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.04425 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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