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

Conference · Mon Apr 25 04:00:00 EDT 2022

OSTI ID:1866734

Dai, Enyan ^[1]; Chen, Jie ^[2]

---

1. Pennsylvania State University; Iowa State University
2. MIT-IBM Watson AI Lab

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.

• Research Organization: Iowa State University, Ames, IA (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: OE0000910
• OSTI ID: 1866734
• Country of Publication: United States
• Language: English