arXiv Machine Learning September 7, 2026 By Adolfo Gonz\'alez

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

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The paper investigates whether forecasting-model selection should be treated as a context-dependent process rather than a universal one. It compares five selection mechanisms across 24 optimized models, nine datasets, and various training-testing partitions and horizons, finding that no single selector dominates in all conditions. The results show that selector performance varies with demand pattern, data availability, and horizon, suggesting a context-dependent approach is more appropriate.

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