KfreqGAN: Unsupervised detection of sequence anomaly with adversarial learning and frequency domain information

Abstract

Sequence anomaly detection in time series is of critical importance to wide applications ranging from finance, healthcare to IT system monitoring. Most current researches use the reconstruction-based deep learning algorithms to solve the problem. In this article, we aim to use a prediction-based method to detect sequence anomalies in univariate time series, because the latter methods can detect anomalies using historical information revealing normal patterns in time series whereas the former methods simply consider current sequences. However, it is challenging because there exists both uncertainty in the future and performance deterioration under long detection horizon. To tackle the challenges, we propose an unsupervised algorithm called KfreqGAN, which is based on adversarially trained sequence predictor. The adversarial learning architecture helps the model make accurate predictions for future sequences. In addition, auxiliary information from frequency domain is used to help the model capture the characteristics of time series for achieving satisfactory predictions. We conduct extensive experiments on two public-available datasets, with results demonstrating the effectiveness of the proposed algorithm and its superiority to baseline algorithms.