用双策略集成提升供应链需求预测的准确性和鲁棒性
Foundation Models for Demand Forecasting via Dual-Strategy Ensembling
- 分层与架构双重集成，分别捕捉不同层级和模型的特征
- 在M5及三个外部数据集上显著优于基线模型
- 适合需要高稳定性与泛化能力的实际供应链场景
精准的需求预测对供应链优化至关重要，但受层级复杂性、领域偏移和外部因素变化影响，实际应用仍具挑战。尽管近期基础模型在时间序列预测中展现出潜力，却常因结构僵化和分布变化下的鲁棒性不足而受限。本文提出一种统一的集成框架，用于增强真实供应链中的销售预测基础模型性能。方法结合两种互补策略：(1) 分层集成（HE），按语义层级（如门店、品类、部门）划分训练与推理，以捕捉局部模式；(2) 架构集成（AE），融合多种模型主干的预测结果，降低偏差并提升稳定性。我们在M5基准和三个外部销售数据集上进行广泛实验，涵盖域内与零样本预测。结果表明，该方法持续超越强基线，提升各层级预测精度，并提供一种简单有效的机制，增强复杂环境中的泛化能力。
原文摘要 · Abstract (English)
Accurate demand forecasting is critical for supply chain optimization, yet remains difficult in practice due to hierarchical complexity, domain shifts, and evolving external factors. While recent foundation models offer strong potential for time series forecasting, they often suffer from architectural rigidity and limited robustness under distributional change. In this paper, we propose a unified ensemble framework that enhances the performance of foundation models for sales forecasting in real-world supply chains. Our method combines two complementary strategies: (1) Hierarchical Ensemble (HE), which partitions training and inference by semantic levels (e.g., store, category, department) to capture localized patterns; and (2) Architectural Ensemble (AE), which integrates predictions from diverse model backbones to mitigate bias and improve stability. We conduct extensive experiments on the M5 benchmark and three external sales datasets, covering both in-domain and zero-shot forecasting. Results show that our approach consistently outperforms strong baselines, improves accuracy across hierarchical levels, and provides a simple yet effective mechanism for boosting generalization in complex forecasting environments.
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