OOA-UADS: offline, online, analysis-an unsupervised anomaly detection solution for multivariate time series

In the era of the Industrial Internet of Things, anomaly detection is important for real-world applications. However, most streaming data lack meaningful labels. Furthermore, some anomalies of streaming data may be concept drift, but few methods can deal with it. To address these challenges, we propose an unsupervised anomaly detection solution that can deal with streaming data, called OOA-UADS (Offline, Online, Analysis-an Unsupervised Anomaly Detection Solution for Multivariate Time Series). The solution consists of three stages: offline training, online prediction and anomaly analysis. Time convolutional networks and variational autoencoders are used to deconstruct and reconstruct the multivariate time series data to learn the normal patterns. The anomaly inversion mechanism identifies concept drift in the anomaly prediction stage by dynamically updating the classification thresholds. Intelligent anomaly analysis then provides anomaly dimensions to help engineers better analyse the anomalous behaviour. Our experiments show that OOA-UADS performs satisfactorily. On seven streaming datasets, OOA-UADS outperforms 11 baselines in terms of AUC and provides state-of-the-art F1 scores on three batch datasets.