Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The vast majority of existing anomaly diagnosis methods offer limited theoretical insights, especially for anomaly localization, which is a vital but largely unexplored area. The aim of this contribution is to study the learning process of a Transformer when applied to MTS by revealing connections to statistical time series methods. Based on these theoretical insights, we propose the Attention Low-Rank Transformer (ALoRa-T) model, which applies low-rank regularization to self-attention, and we introduce the Attention Low-Rank score, effectively capturing the temporal characteristics of anomalies. Finally, to enable anomaly localization, we propose the ALoRa-Loc method, a novel approach that associates anomalies to specific variables by quantifying interrelationships among time series. Extensive experiments and real data analysis, show that the proposed methodology significantly outperforms state-of-the-art methods in both detection and localization tasks.

翻译：多元时间序列异常诊断（包括异常检测与定位）对于复杂大规模现实系统的安全性与可靠性至关重要。现有绝大多数异常诊断方法提供的理论见解有限，尤其在异常定位这一关键但尚未充分探索的领域。本文旨在通过揭示Transformer应用于多元时间序列时与统计时间序列方法的关联，研究其学习过程。基于这些理论洞见，我们提出注意力低秩Transformer模型，该模型对自注意力机制施加低秩正则化，并引入注意力低秩评分以有效捕捉异常的时间特性。最后，为实现异常定位，我们提出ALoRa-Loc方法——通过量化时间序列间相互关联性将异常关联至特定变量的创新方法。大量实验与真实数据分析表明，所提方法在检测与定位任务上均显著优于现有最优方法。