MultiverseAD: Enhancing spatial-temporal synchronous attention networks with causal knowledge for multivariate time series anomaly detection.

Abstract

Multivariate time series anomaly detection is crucial for various real-world applications to guarantee system reliability and safety. Many deep learning methods have been proposed for time series anomaly detection purposes. However, most existing approaches treat spatial and temporal features separately, limiting their capabilities to cope with complex interactions in real-world applications. In this study, we introduce MultiverseAD, a spatial-temporal synchronous attention network that incorporates causal knowledge to enhance anomaly detection performance in multivariate time series. MultiverseAD combines a dynamic spatial-temporal synchronous attention network with a static spatial-temporal causal graph. The network employs sliding graph attention to capture local and long-term dependencies, while the causal graph encodes static causal relationships to enhance the learning process. Experiments on eight public datasets have shown that MultiverseAD consistently surpasses twelve state-of-the-art models. Ablation experiment results further validate the effectiveness of both the causal graph and the synchronous attention network.