让时间序列预测更抗异常，关键在对比学习加权。
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
- 用加权对比学习对齐正常与异常数据表示
- 异常数据上SMAPE提升6.1个百分点，正常数据几乎无损
- 适合金融、物流等需应对突发波动的场景
在ATM现金调度等应用中，异常条件下的多变量时间序列可靠预测至关重要。现代深度预测模型在正常数据上表现优异，但在分布偏移时往往失效。我们提出加权对比适应（WECA），一种加权对比目标，通过对齐正常与异常增强的表示，保留异常相关特征的同时维持良性变化下的一致性。在包含领域知情异常注入的全国性ATM交易数据集上评估显示，与常规训练基线相比，WECA在异常影响数据上的SMAPE提升6.1个百分点，且对正常数据性能几乎没有下降。结果表明，WECA在不牺牲常规运行表现的前提下提升了异常情况下的预测可靠性。
原文摘要 · Abstract (English)
Reliable forecasting of multivariate time series under anomalous conditions is crucial in applications such as ATM cash logistics, where sudden demand shifts can disrupt operations. Modern deep forecasters achieve high accuracy on normal data but often fail when distribution shifts occur. We propose Weighted Contrastive Adaptation (WECA), a Weighted contrastive objective that aligns normal and anomaly-augmented representations, preserving anomaly-relevant information while maintaining consistency under benign variations. Evaluations on a nationwide ATM transaction dataset with domain-informed anomaly injection show that WECA improves SMAPE on anomaly-affected data by 6.1 percentage points compared to a normally trained baseline, with negligible degradation on normal data. These results demonstrate that WECA enhances forecasting reliability under anomalies without sacrificing performance during regular operations.
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