GCAD：从格兰杰因果视角进行多元时间序列异常检测
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
AI总结：
本文提出GCAD框架，利用可解释的格兰杰因果关系建模多元时间序列的空间依赖，并通过因果模式变化检测异常，在真实数据集上优于基线方法。
摘要：
多元时间序列异常检测具有众多现实应用，并受到广泛研究。对变量间的成对相关性进行建模至关重要。现有方法采用可学习的图结构和图神经网络来显式建模变量间的空间依赖关系。然而，这些方法主要基于预测或重构任务，只能学习序列嵌入之间的相似性关系，并且缺乏图结构如何影响时间序列演化的可解释性。在本文中，我们设计了一个框架，使用可解释的因果关系来建模空间依赖，并通过因果模式的变化来检测异常。具体来说，我们提出了一种方法，利用非线性深度预测器中的梯度动态发现格兰杰因果关系，并采用简单的稀疏化策略获得格兰杰因果图，从因果视角检测异常。在真实世界数据集上的实验表明，与基线方法相比，所提出的模型实现了更准确的异常检测。
英文摘要：
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.
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。
