VAEAT: Variational AutoeEncoder with adversarial training for multivariate time series anomaly detection

High labor costs and the requirement for significant domain expertise often result in a lack of anomaly labels in most time series. Consequently, employing unsupervised methods becomes critical for practical industrial applications. However, prevailing reconstruction-based anomaly detection algorithms encounter challenges in capturing intricate underlying correlations and temporal dependencies in time series. This study introduces an unsupervised anomaly detection model called Variational AutoeEncoder with Adversarial Training for Multivariate Time Series Anomaly Detection (VAEAT). Its fundamental concept involves adopting a two-phase training strategy to improve anomaly detection precision through adversarial reconstruction of raw data. In the first phase, the model reconstructs raw data to extract its basic features by training two enhanced variational autoencoders (VAEs) that incorporate both the long short -term memory (LSTM) network and the attention mechanism in their common encoder. In the second phase, the model refines reconstructed data to optimize the reconstruction quality. In this manner, this two-phase VAE model effectively captures intricate underlying correlation and temporal dependencies. A large number of experiments are conducted to evaluate the performance on five publicly available datasets, and experimental results illustrate that VAEAT exhibits robust performance and effective anomaly detection capabilities. The source code of the proposed VAEAT can be available at https://github .com /Du -Team /VAEAT.