DriftGuard：一种用于供应链预测中概念漂移检测与修复的分层框架
DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting
AI总结：
DriftGuard通过分层框架实现供应链预测中的概念漂移检测与修复，结合多种检测方法和SHAP分析，提升模型鲁棒性和修复效率。
摘要：
供应链预测模型随着时间推移会因现实条件的变化而退化。促销活动变化、消费者偏好演变以及供应中断改变需求模式，导致所谓的概念漂移。这种无声的退化会导致缺货或库存过剩，而不会触发任何系统警告。当前行业实践依赖于人工监控和每3-6个月一次的定期重新训练，这在稳定时期浪费计算资源，同时错过快速漂移事件。现有的学术方法专注于狭窄的漂移检测，未解决诊断或修复问题，并忽略了供应链数据中固有的分层结构。零售商需要的是一个端到端的系统，能够早期检测漂移、解释其根本原因并自动修正受影响的模型。我们提出了DriftGuard，一个五模块框架，解决了完整的漂移生命周期。该系统结合了四种互补的检测方法的集成，即基于误差的监控、统计检验、自编码器异常检测和累积和（CUSUM）变化点分析，以及分层传播分析，以确定漂移在不同产品线中的确切位置。一旦检测到，Shapley Additive Explanations（SHAP）分析诊断根本原因，而一种成本敏感的重新训练策略会只更新最受影响的模型。在M5零售数据集中的30000多个时间序列上进行评估，DriftGuard在4.2天内实现了97.8%的检测召回率，并通过有针对性的修复带来高达417的回报率。
英文摘要：
Supply chain forecasting models degrade over time as real-world conditions change. Promotions shift, consumer preferences evolve, and supply disruptions alter demand patterns, causing what is known as concept drift. This silent degradation leads to stockouts or excess inventory without triggering any system warnings. Current industry practice relies on manual monitoring and scheduled retraining every 3-6 months, which wastes computational resources during stable periods while missing rapid drift events. Existing academic methods focus narrowly on drift detection without addressing diagnosis or remediation, and they ignore the hierarchical structure inherent in supply chain data. What retailers need is an end-to-end system that detects drift early, explains its root causes, and automatically corrects affected models. We propose DriftGuard, a five-module framework that addresses the complete drift lifecycle. The system combines an ensemble of four complementary detection methods, namely error-based monitoring, statistical tests, autoencoder anomaly detection, and Cumulative Sum (CUSUM) change-point analysis, with hierarchical propagation analysis to identify exactly where drift occurs across product lines. Once detected, Shapley Additive Explanations (SHAP) analysis diagnoses the root causes, and a cost-aware retraining strategy selectively updates only the most affected models. Evaluated on over 30,000 time series from the M5 retail dataset, DriftGuard achieves 97.8% detection recall within 4.2 days and delivers up to 417 return on investment through targeted remediation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
