Multi-Graph Structure Learning-based Multivariate Time Series Anomaly Detection with Extended Prior Knowledge

In the Internet of Things (IoT), substantial time series data is recorded by sensors and other devices. Multivariate time series anomaly detection (MTSAD) identifies anomalies derived from device malfunctioning or system attacks to reduce economic losses. Graph structure learning (GSL)-based anomaly detection method learns an optimal graph structure joint with the downstream anomaly detection task, which achieves superior performance. However, the existing GSL-based methods only learn a single graph structure and can not represent multiple and complex relationships. Therefore, we propose a multi-graph structure learning-based multivariate time series anomaly detection with extended prior knowledge (MEGLAD). MEGLAD selects three kinds of typical graph structure learners to learn as many relationship types among sensors as possible. Extensive experiments show that our approach has better detection performance than state-of-the-art single graph structure learning techniques on four public and real-world datasets.