VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data

Abstract

Time-series anomaly detection holds value across various research fields and application domains, serving purposes such as fault diagnosis, identification of unexpected system intrusions, or signaling the onset of new disease outbreaks. To identify anomalous timepoints in a practical manner, unsupervised learning methods have been introduced. These methods leverage normality’s feature representations to reconstruct data, determining anomalies through the calculation of anomaly scores based on reconstruction errors. Still, recent approaches assume the use of a normal dataset for model training, implicitly necessitating true anomaly labels. Furthermore, the absence of guidelines for determining anomaly thresholds hinders the easy application of current detection methods in diverse research fields. In this paper, we propose VRGAN, a deep generative anomaly detection framework designed for unsupervised multivariate time-series analysis. VRGAN stands out by eliminating the need for assumptions or true label information. Through the training of variational recurrent neural network and GAN modules on a training dataset masked for anomaly candidates, VRGAN reconstructs the dataset based on learned normal data distribution and calculates anomaly scores to identify anomalies. Evaluation on seven benchmark datasets demonstrates VRGAN’s performance improvement compared with recent unsupervised anomaly detection methods. The proposed model is further tested on a time-series metagenomic dataset, showcasing its applicability in fully unsupervised settings for wastewater-based surveillance to monitor and track patterns of antibiotic resistance genes that are prevalent among bacteria carried by a given human community.