Business environments characterized by structural demand intermittency, high variability, and multi-step planning horizons require robust and reproducible model selection mechanisms. Empirical evidence shows that no forecasting model is universally dominant and that relative rankings vary across error metrics, demand regimes, and forecast horizons, generating ambiguity in multi-SKU decision contexts. This study proposes AHSIV (Adaptive Hybrid Selector for Intermittency and Variability), a horizon-aware and regime-conditioned model selection framework designed to address horizon-induced ranking instability. The proposed approach integrates scaled and absolute error metrics adjusted through a Metric Degradation by Forecast Horizon (MDFH) procedure, structural demand classification, multi-objective Pareto dominance, and hierarchical bias refinement within a unified decision architecture. The empirical evaluation is conducted on the Walmart, M3, M4, and M5 datasets under multiple train-test partition schemes and twelve-step forecasting horizons. Results indicate that AHSIV achieves statistical equivalence with the strongest monometric baseline in terms of aggregated performance while increasing the frequency of horizon-specific best-model selection. The findings demonstrate that model selection in heterogeneous demand environments cannot be treated as a static ranking problem, and that horizon-consistent, structurally adaptive mechanisms provide a principled, operationally coherent solution for multi-SKU forecasting.

翻译：在结构性需求间歇性、高波动性和多步规划时域为特征的商业环境中，需要稳健且可复现的模型选择机制。实证证据表明，不存在普遍占优的预测模型，且模型的相对排名会随误差度量指标、需求状态和预测时域而变化，这在多SKU决策场景中产生了模糊性。本研究提出了AHSIV（面向间歇性与波动性的自适应混合选择器），这是一个时域感知且状态条件化的模型选择框架，旨在解决由预测时域引起的排名不稳定性。该方法在一个统一的决策架构中，集成了通过"预测时域度量退化"（MDFH）程序调整的标度与绝对误差度量、结构性需求分类、多目标帕累托占优以及分层偏差修正。实证评估在Walmart、M3、M4和M5数据集上进行，采用了多种训练-测试划分方案和十二步预测时域。结果表明，AHSIV在聚合性能方面与最强的单度量基线模型达到了统计等效，同时提高了针对特定时域选择最佳模型的频率。研究结果表明，在异质性需求环境中的模型选择不能被视为静态排名问题，而时域一致、结构自适应的机制为多SKU预测提供了一种原则性且运营连贯的解决方案。