Multivariate Time Series Anomaly Detection With Generative Adversarial Networks Based on Active Distortion Transformer

Detecting anomalies for multivariate time series is of great importance in modern industrial applications. However, due to the complex temporal dynamics in modern systems, finding a distinguishable judge criterion is hard, which makes accurate anomaly detection still a challenging task. In order to better capture the anomalous features and design a more informative judge criterion, this article presents an unsupervised generative adversarial network (GAN) for multivariate time series anomaly detection, which highlights a novel active distortion transformer (ADT) block. Different from the vanilla transformer, the ADT block can make good use of the prior knowledge of time sequences' overall associations by actively conducting distortion during the reconstruction of input sequences. Benefiting from the ADT block, the network simultaneously utilizes the sequence associations and reconstruction error to recognize anomalies. In the online detection phase, anomalous data points tend to be less correlated with the overall sequence and have greater reconstruction errors than normal ones, so that an irrelevance score and a reconstruction error score can be obtained. We combine the two scores to generate a more powerful anomaly score as the judge criterion. Extensive experiments are conducted on four publicly available sensor datasets, and we also make comparisons with the recent baselines. Results show that our model outperforms the recent state-of-the-art methods, demonstrating the effectiveness of our method.