Authors
- Jie Chen
- Enyan Dai
Published on
04/29/2022
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.
Please cite our work using the BibTeX below.
@inproceedings{
dai2022graphaugmented,
title={Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series},
author={Enyan Dai and Jie Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=45L_dgP48Vd}
}