Granger Causality for Time-Series Anomaly Detection

Recent developments in industrial systems provide us with a large amount of time series data from sensors, logs, system settings and physical measurements, etc. These data are extremely valuable for providing insights about the complex systems and could be used to detect anomalies at early stages. However, the special characteristics of these time series data, such as high dimensions and complex dependencies between variables, as well as its massive volume, pose great challenges to existing anomaly detection algorithms. In this paper, we propose Granger graphical models as an effective and scalable approach for anomaly detection whose results can be readily interpreted. Specifically, Granger graphical models are a family of graphical models that exploit the temporal dependencies between variables by applying L1-regularized learning to Granger causality. Our goal is to efficiently compute a robust "correlation anomaly" score for each variable via Granger graphical models that can provide insights on the possible reasons of anomalies. We evaluate the effectiveness of our proposed algorithms on both synthetic and application datasets. The results show the proposed algorithm achieves significantly better performance than other baseline algorithms and is scalable for large-scale applications.