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

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On September 7, 2026, arXiv cs.LG published the research paper “Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons”. This study proposed a new model selection framework aimed at addressing the issue of insufficient adaptability of universal predictors under different demand patterns and forecast durations. By introducing a “demand condition” mechanism, the method dynamically adjusts the selected forecasting models based on specific demand pattern characteristics and forecast time spans, thereby improving prediction accuracy in various scenarios.

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A arXiv cs.LG en 2026-09-07 12:00

This study proposes a method for selecting demand-conditioning models, comparing the performance of five mechanisms: RMSSE, ERA, OWA, CCG-AHSC, and CCG-AHSCD, across 24 optimization models, 9 datasets, and cycles ranging from 1 to 12. The evaluation results show that there is no selector that outperforms all conditions: CCG-AHSC and CCG-AHSCD are more competitive in smoothing demand and various erratic configurations, while OWA and ERA perform better in intermittent and block-based settings. Additionally, the applicability of the selectors varies with the availability of historical data and the duration of predictions, supporting the use of context-dependent rather than universal methods for selecting prediction models.