Hierarchical Context Representation and Self-adaptive Thresholding for Multivariate Anomaly Detection

Anomaly detection in multivariate time series is a critical research area, but it is also a challenging one due to its occurrence in various real-world scenarios, such as structural health monitoring and risk management. Traditional approaches for anomaly detection rely on deviating distribution and a static threshold that is set manually. However, static thresholds fail to detect contextual anomalies, leading to a high ratio of false anomalies. Therefore, a self-adaptive thresholding method is required to improve the accuracy of anomaly detection. In this study, we propose HCR-AdaAD, a multivariate anomaly detection framework that combines hierarchical context representation learning with deep learning methods. The core idea is to extract normal time-series patterns by transforming them into images, which can be used to extract spatial features and generate robust representations for normal time series. Next, we adopt Extreme Value Theory (EVT) to set self-adaptive thresholds in streaming time series, which can contribute to the ideal precision for anomaly detection and high interpretability with contextual information. We conducted evaluation experiments on three public datasets, and the results demonstrate the effectiveness and soundness of our proposed model. HCR-AdaAD offers a novel and effective approach to anomaly detection in multivariate time series that outperforms traditional methods, making it a promising solution for real-world applications in various domains.