CAAD：通过多尺度对齐和结构因果一致性进行因果感知多变量时间序列异常检测
CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency
AI总结：
针对复杂工业系统异常检测中常忽略内部因果关系的问题，提出CAAD框架，通过外生变量持续验证格兰杰因果一致性，利用多尺度对齐和梯度矩阵监测因果关系，在真实工业数据集实验中实现高精度异常检测，优于多数基线方法。
摘要：
复杂工业系统的运行完整性依赖于精确的异常检测和诊断。现有的绝大多数方法都局限于捕捉表征的时间相似性，常常忽略内部因果关系的破坏，而这正是系统故障和潜在异常的特征。在本文中，我们提出了一个新颖的框架（CAAD），将异常检测重新定义为通过外生变量对格兰杰因果一致性的持续验证。具体而言，CAAD框架将外生时间序列变量建模为残差，将异常识别为由外部干预导致的显著偏差。该框架利用多尺度对齐来内化系统动态，并使用基于梯度的矩阵来监测内部因果关系的崩溃。通过量化动态演化和关系拓扑的因果偏差，CAAD能够捕捉细微的因果变化以实现精确的异常检测。在真实工业数据集上的大量实验表明，CAAD实现了高精度的异常检测，优于大多数最先进的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
