Rethinking Robust Multivariate Time Series Anomaly Detection: A Hierarchical Spatio-Temporal Variational Perspective

Abstract

The robust multivariate time series anomaly detection can facilitate intelligent decisions and timely maintenance in various kinds of monitor systems. However, the robustness is highly restricted by the stochasticity in multivariate time series, which is summarized as temporal stochasticity and spatial stochasticity specifically. In this paper, we explicitly model the temporal stochasticity variables and the latent graph relationship variables into a unified graphical framework, which can achieve better robustness to dynamicity from both the spatial and temporal perspective. First, within the spatial encoder, every connection exists or not is modeled as a binary stochastic variable, and the graph structure can be learnt automatically. Then, the temporal encoder would embed the highly structured time series into latent stochastic variables to capture both complex temporal dependencies and neighbors information. Moreover, we design a history-future combined anomaly score mechanism with both reconstruction decoder and forecasting decoder to improve the anomaly detection performance. By weighting the historical anomaly factor, the future anomaly factor, and the prediction error of current timestamp, the anomaly detection at current timestamp could be more sensitive to anomaly detection. Finally, extensive experiments on three publicly available anomaly detection datasets demonstrate our proposed method can achieve the best performance in terms of recall and F1 compared with state-of-the-arts baselines.