通过因果一致性检测，精准识别工业系统中的异常信号。
CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency
- 利用外生变量残差建模，从因果关系角度捕捉异常
- 多尺度对齐与梯度矩阵结合，提升对细微因果偏移的敏感度
- 适合关注系统内在机制的工业故障检测场景
复杂工业系统的运行完整性依赖于精确的异常检测与诊断。现有方法多聚焦于表征的时间相似性，常忽略系统内部因果关系的破坏，而这种破坏正是系统故障和潜在异常的特征。本文提出一种新框架CAAD，将异常检测重构为通过外生变量持续验证格兰杰因果一致性。具体而言，CAAD将外生时间序列变量建模为残差，将异常视为外部干预引起的显著偏离。该框架采用多尺度对齐内化系统动态，并利用基于梯度的矩阵监控内部因果关系的破裂。通过量化动态演化与关系拓扑的因果偏差，CAAD能够捕捉细微的因果变化，实现精准异常检测。在真实工业数据集上的大量实验表明，CAAD在高精度异常检测上优于多数主流基线方法。
原文摘要 · Abstract (English)
The operational integrity of complex industrial systems relies on precise anomaly detection and diagnosis. The vast majority of existing methods narrowly focus on capturing temporal similarities of representations, often overlooking the disruption of internal causal relationships, which characterizes system failures and latent anomalies. In this paper, we propose a novel framework (CAAD) that reframes anomaly detection as the continuous verification of Granger causality consistency through exogenous variables. Specifically, the CAAD framework models exogenous time-series variables as residuals, identifying anomalies as significant deviations caused by external interventions. The proposed framework leverages multi-scale alignment to internalize system dynamics and utilizes a gradient-based matrix to monitor internal causal relationship breakdowns. By quantifying causal deviations of both dynamic evolution and relational topology, the CAAD is able to capture subtle causal shifts to achieve precise anomaly detection. Extensive experiments on real-world industrial datasets demonstrate that the CAAD achieves high-precision anomaly detection, outperforming most state-of-the-art baselines.
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