Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection

Abstract

Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data. The de-facto solution to model the dependencies is to feed the data into a recurrent neural network (RNN). However, the fully connected network structure underneath the RNN (either GRU or LSTM) assumes a static and complete dependency graph between time-series, which may not hold in many real-world applications. To alleviate this assumption, we propose a dynamic bipartite graph structure to encode the inter-dependencies between time-series. More concretely, we model time series as one type of nodes, and the time series segments (regarded as event) as another type of nodes, where the edge between two types of nodes describe a temporal pattern occurred on a specific time series at a certain time. Based on this design, relations between time series can be explicitly modelled via dynamic connections to event nodes, and the multivariate time-series anomaly detection problem can be formulated as a self-supervised, edge stream prediction problem in dynamic graphs. We conducted extensive experiments to demonstrate the effectiveness of the design.

Hybrid Anomaly Detection via Multihead Dynamic Graph Attention Networks for Multivariate Time Series

“…The reconstruction errors can serve as anomaly scores for detecting anomalies. Wu et al [38] proposed Event2Graph, which uses a dynamic bipartite graph structure to capture the interdependencies between observations. Event2Graph converts the predicted event edges into anomaly scores.…”