发表机构
University of Santiago of Chile（智利圣地亚哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对异质性需求下预测模型选择困难的问题，提出将选择器视为预测过程的上下文依赖组件，对比5种选择机制在多模型、多数据集等条件下的表现，验证了需求条件化模型选择的必要性。
AI 中文摘要
在异质性需求场景中，预测模型选择仍存在困难，因为最合适的决策规则可能随需求结构、数据可用性和预测周期变化。本研究探讨是否应将选择器本身视为预测过程中依赖上下文的组件。在24种优化预测模型、9个数据集、3种训练-测试划分及1至12周期的预测周期范围内，对比了RMSSE、ERA、OWA、CCG-AHSC和CCG-AHSCD这5种选择机制。采用全局相对精度（GRA）、统计检验及最佳可获得模型参考，事后评估选择器性能。结果显示，无选择器在所有条件下占优；CCG-AHSC和CCG-AHSCD在平稳需求及若干不规则配置中更具竞争力，而OWA和ERA在间歇及块状需求场景中表现更佳。选择器适用性也随历史数据可用性和周期变化，支持预测模型选择应采用依赖上下文而非通用的方法。
英文摘要
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.
Comments30 pages, 7 figures
