DoKnowAD: Calibrating Normal Representations with Refined Domain Knowledge to Enhance Time Series Anomaly Detection

Abstract

Time series anomaly detection (TSAD) is critical in various real-world applications. Due to the high cost of manual annotation, unsupervised methods are commonly employed to distinguish abnormal patterns from normal ones based on data or representation characteristics. However, the limited coverage of a single dataset often leads to misclassifying test-time normal patterns that deviate from the training distribution as anomalies. In view of this, we propose to introduce domain knowledge from auxiliary datasets (AuxSets) to enhance domain-level normality understanding in the target dataset (TargetSet). However, through in-depth analysis on the representation space of the TargetSet after incorporating AuxSets, we find that consistent knowledge about normality from homogeneous AuxSets do little help to TargetSet, while diverse knowledge from heterogeneous AuxSets can bring semantic confusion of normality for TargetSet, both of which can degrade TargetSet detection performance. To address the issue, we design DoKnowAD, a framework that introduces a Representation HyperVolume Estimation metric to identify helpful heterogeneous AuxSets, and further adopts contrastive learning to enforce loose coupling between datasets and high cohesion within single dataset to calibrate the TargetSet’s representation space, thus mitigating knowledge confusion. Extensive experiments on five popular datasets across different domains demonstrate that DoKnowAD consistently outperforms existing TSAD baselines in various metrics.