A graph attention network-based model for anomaly detection in multivariate time series

Anomaly detection of multivariate time series plays a growingly crucial role in intelligent operation and maintenance. Most existing anomaly detection models tend to focus on extracting temporal information while essentially ignoring the relationships among multiple sensors. Graph neural networks simulate multivariate inter-series relationships but suffer from the independent updating of graph structure from data. To overcome such limitation, we present a graph attention network-based model to learn the sensors relationships. It is equipped with a sustainable updating similarity-constrained graph structure learning method and a time series encoder. The graph learning method performs continuous updating of the graph structure. The encoder generates augmented and representative views along the temporal dimension. Our proposed model not only efficiently learns sensor relationships but also improves the ability of anomaly detection. Experiments are performed on publicly benchmark datasets, including SWaT, WADI, and HAI, with F 1 scores of 92.98%, 91.19%, and 87.12%, respectively. This confirms that the proposed model outperforms the state-of-the-art in anomaly detection performance.