AgileAD: Anchor-Guided Contrastive Learning with a General Data Augmentation Strategy for Time Series Anomaly Detection

Multivariate time series anomaly detection (MT-SAD) plays a crucial role for the Internet of Things (IoT) systems. Various IoT systems rely on time series to monitor and identify anomalies, as well as to initiate remediation procedures. Existing contrastive learning methods for MTSAD have strong ability to learn invariant representations from augmented views. However, these methods rely on special data augmentation strategies and may not be suitable for certain time series. Besides, when abnormal points intensively occur in a segment, the similarity representations they learned from augmented views would make normal and abnormal points indistinguishable. To address these issues, we propose a novel Anchor-guided contrastive learning method with a general data augmentation strategy for multivariate time series Anomaly Detection (AgileAD). Specifically, this general strategy is a simple yet efficient data augmentation strategy to shuffle each univariate time series (i.e., metric) vertically. It can fit any time series to create different but correlated views. To improve the model discrimination in the presence of anomaly aggregation, AgileAD utilizes an anchor-guided contrastive structure module to learn the anchor representations of the raw input and capture temporal and intermetric dependencies of time series. Moreover, the triplet representation discrepancy is designed to promote the learning of shuffle invariant representations. It cooperates with the guided module to effectively distinguish between normal and abnormal points. Extensive experiments show that AgileAD achieves state-of-the-art results on multiple IoT benchmark datasets.