联邦集成预测模型提升供应链在波动市场下的预报精度与可靠性。
Federated Ensemble Forecasting Under Supply-Chain Market Volatility
- 通过负相关学习让各节点专家模型差异互补，避免重复错误。
- 误差降低1.5个百分点，高波动区表现更优，宏观F1提升4.6%。
- 适合需要隐私保护的多供应商协同需求预测场景。
供应链预测系统面临市场冲击、区域需求分布不均及数据难以集中等问题。本文提出联邦集成预测与负相关学习（FEF NCL）框架，在客户端训练专用预测专家，通过负相关机制抑制模型冗余误差。该方法融合时序特征编码、客户端漂移评分、可靠性加权聚合及可解释层，揭示影响预测的关键市场与供应商变量。使用合成数据集评估：包含124,800条每周SKU-区域观测数据，来自10个区域节点，涵盖60个产品族、40家供应商、5类商品，以及2021–2024年具有明确价格冲击阶段的波动性特征。在合成测试集上，相比最优联邦基线（13.9%），FEF NCL将加权平均绝对百分比误差降至12.4%，延迟风险宏F1从0.755提升至0.801，高波动五分位误差降低2.1个百分点。结果表明负相关专业化在不同供应、运输与商品条件下有效，但实际部署需强化隐私分析、实时漂移监控与运营校准。
原文摘要 · Abstract (English)
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
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