Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

• URL: http://arxiv.org/abs/2202.07857v1
• Date: Wed, 16 Feb 2022 04:42:53 GMT
• Title: Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
• Authors: Enyan Dai, Jie Chen
• Abstract summary: Anomaly detection is a widely studied task for a broad variety of data types. We propose a graph-augmented normalizing flow approach for anomaly detection. We conduct experiments on real-world datasets and demonstrate the effectiveness of GANF.
• Score: 12.745860899424532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

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