供应链市场波动下的联邦集成预测
Federated Ensemble Forecasting Under Supply-Chain Market Volatility
AI总结：
本研究针对供应链市场波动下的预测需求，提出FEF NCL分布式方法，在合成数据集上实现了预测误差降低、延迟风险宏观F1提升及高波动误差减少的效果，为供应链预测提供了可行方案。
AI中文摘要：
供应链预测系统越来越多地在市场冲击、非同质区域需求以及商业数据集中意愿有限的环境下运行。本研究提出了带负相关学习的联邦集成预测（FEF NCL），这是一种分布式方法，可在客户端节点上训练专门的预测专家，同时抑制冗余模型误差。该框架结合了时间特征编码器、客户端层面漂移评分、可靠性加权聚合以及可解释性层，该层揭示了对每次预测影响最大的市场和供应商变量。研究使用一个合成数据集评估该设计，该数据集包含来自10个区域客户端节点的124800个每周SKU区域观测值，涵盖60个产品系列、40个供应商、5个商品组，以及带有明确价格冲击机制的2021-2024年波动概况。由于该数据集是合成的，报告的结果应被解释为内部一致性的受控证据，而非现实世界的验证。在合成测试拆分中，FEF NCL将加权平均绝对百分比误差从最佳联邦基线的13.9%降至12.4%，将延迟风险宏观F1从0.755提高到0.801，并将高波动五分位数误差相对于SCAFFOLD降低了2.1个百分点。分析表明，当客户端面临不同的供应商、货运和商品条件时，负相关专业化是有用的，尽管部署需要更强的隐私分析、实时漂移监测和操作校准。
英文摘要：
Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to centralize commercial data. This work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts across client nodes while discouraging redundant model errors. The framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the market and supplier variables most responsible for each forecast. A single synthetic dataset is used to evaluate the design. It contains 124,800 weekly SKU region observations from ten regional client nodes, 60 product families, 40 suppliers, five commodity groups, and a 2021-2024 volatility profile with explicit price-shock regimes. Because the dataset is synthetic, the reported results should be interpreted as controlled evidence of internal consistency rather than real-world validation. Across the synthetic test split, FEF NCL reduces weighted mean absolute percentage error from 13.9% for the best federated baseline to 12.4%, improves delay-risk macro-F1 from 0.755 to 0.801, and lowers the high volatility quintile error by 2.1 percentage points relative to SCAFFOLD. The analysis suggests that negative-correlation specialization is useful when clients face different supplier, freight, and commodity conditions, although deployment would require stronger privacy analysis, live drift monitoring, and operational calibration. Index Terms federated learning, ensemble learning, negative correlation learning, supply chain forecasting, market volatility, data drift, demand planning, risk governance
