GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
This work addresses the need for interpretable anomaly detection in multivariate time series for applications like monitoring and diagnostics, though it is incremental as it builds on existing graph-based methods by adding causal modeling.
The paper tackles the problem of anomaly detection in multivariate time series by modeling spatial dependencies with interpretable causal relationships, specifically using Granger causality, and detects anomalies through changes in causal patterns. The result is a model that achieves more accurate anomaly detection compared to baseline methods on real-world datasets.
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.