用因果关系检测多变量时间序列异常，更可解释且更准确。
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
- 通过梯度动态发现非线性模型中的格兰杰因果关系
- 构建稀疏因果图，异常由因果模式变化触发检测
- 适合需要可解释性的工业监控与金融风控场景
多变量时间序列异常检测在现实应用中广泛存在，建模变量间的成对相关性至关重要。现有方法多采用可学习图结构与图神经网络显式建模变量间的空间依赖，但主要基于预测或重构任务，仅能学习序列嵌入间的相似性，缺乏图结构如何影响时序演化的可解释性。本文提出一种从可解释因果关系视角建模空间依赖的框架，通过非线性深度预测器的梯度动态发现格兰杰因果关系，并采用简单稀疏化策略构建格兰杰因果图，从因果模式变化角度检测异常。在真实数据集上的实验表明，所提模型相比基线方法实现了更精确的异常检测。
原文摘要 · Abstract (English)
Multivariate time series anomaly detection has numerous real-world applications and is being extensively studied. Modeling pairwise correlations between variables is crucial. Existing methods employ learnable graph structures and graph neural networks to explicitly model the spatial dependencies between variables. However, these methods are primarily based on prediction or reconstruction tasks, which can only learn similarity relationships between sequence embeddings and lack interpretability in how graph structures affect time series evolution. In this paper, we designed a framework that models spatial dependencies using interpretable causal relationships and detects anomalies through changes in causal patterns. Specifically, we propose a method to dynamically discover Granger causality using gradients in nonlinear deep predictors and employ a simple sparsification strategy to obtain a Granger causality graph, detecting anomalies from a causal perspective. Experiments on real-world datasets demonstrate that the proposed model achieves more accurate anomaly detection compared to baseline methods.
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