不同需求场景下，没有万能模型选择器，需按情况选。
Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons
- 根据需求特征动态选择最适合的模型选择器
- CCG-AHSC/D在平稳和波动需求中表现更好
- 适合需要精准预测的供应链、零售等领域
需求异质性使模型选择困难，因最优决策规则随需求结构、数据量和预测周期变化。本研究检验选择器是否应作为上下文依赖组件。对比了五种选择机制（RMSSE、ERA、OWA、CCG-AHSC、CCG-AHSCD），在24个优化模型、9个数据集、3种训练测试划分及1至12周期的预测范围内评估。采用全局相对准确率（GRA）、统计检验和最佳可达成模型作为基准。结果表明无单一选择器在所有条件下占优：CCG-AHSC与CCG-AHSCD在平稳与部分波动场景更优；OWA与ERA在间歇性和不规则场景表现更佳。选择器适用性也随历史数据量与预测周期变化，支持上下文依赖而非通用的模型选择方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。