OSVAE-GAN: Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks for Time Series Anomaly Detection

Time series anomaly detection is a binary classification problem with unbalanced data, which aims to identify data that fall outside of the normal behaviors. Since the proportion of the abnormal data is very small, the cost of labeling all data is prohibitively high. Therefore, unsupervised methods are more suitable than supervised methods. With the rapid development of deep learning, various multivariate time series anomaly detection methods based on deep learning have been proposed. However, existing methods do not fully capture the spatial-temporal correlations and are not robust to noise. To address these issues, we propose an unsupervised anomaly detection method called Orthogonal Self-Attention Variational Autoencoder Generative Adversarial Networks (OSVAE-GAN). To fully extract the spatial-temporal correlations, we use an orthogonal self-attention (OS) mechanism. Moreover, to increase the capability to deal with complex multivariate data, we integrate two generative adversarial networks (GANs) with the variational autoencoder (VAE). Finally, to reduce the influence of noise, we introduce the maximum mean discrepancy (MMD) loss. Experiments are conducted on five public datasets, which show that the proposed method is superior to the existing methods.