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.

翻译：在异常条件下对多元时间序列进行可靠预测，对于诸如ATM现金物流等应用至关重要，其中需求的突然变化可能扰乱运营。现代深度预测模型在正常数据上能达到高精度，但在分布偏移发生时往往失效。我们提出加权对比适应（WECA），这是一种加权对比目标，旨在对齐正常与异常增强的表征，在保持良性变化下一致性的同时，保留异常相关信息。基于全国ATM交易数据集并注入领域知识驱动的异常进行的评估表明，与正常训练的基线相比，WECA在受异常影响的数据上将SMAPE提升了6.1个百分点，而在正常数据上的性能下降可忽略不计。这些结果证明，WECA在增强异常下预测可靠性的同时，未牺牲常规运营期间的性能。