Trajectory-Guided Supervision Rectification with Prototype-Repulsive Extrapolation for Open-Set Time Series Anomaly Detection

Time series anomaly detection (TSAD) aims to identify abnormal temporal patterns in sequential data. In real-world scenarios, anomaly annotations are often restricted to a subset of anomaly types, and open-set TSAD is therefore critical for detecting both seen and unseen anomalies using limited labeled examples. Despite the progress, existing approaches typically rely on simple augmentation that provides limited coverage of unseen patterns and tend to overfit seen patterns when nominally normal training data are contaminated by unlabeled anomalies. Towards this end, we propose a novel approach named $\underline{T}$rajectory-G$\underline{u}$ided Supervision $\underline{R}$ectification with $\underline{P}$rototype-Repu$\underline{l}$sive $\underline{E}$xtrapolation (TURPLE) for open-set TSAD. The core of our TURPLE is to jointly refine contaminated supervision and expand anomaly coverage beyond seen patterns. Specifically, given the pretrained deviation-aware detector with a seen head, we track perturbation-induced deviation trajectories during training to identify latent anomalies among normal samples and rectify their labels. Building on the refined supervision, we construct normal and anomaly prototypes and introduce a prototype-repulsive extrapolation that encourages unseen anomaly prototypes beyond observed patterns. Finally, an unseen head is trained with adaptive sample-level gating on refined normal samples, seen anomalies, and pseudo-unseen anomalies. Extensive experiments demonstrate the effectiveness of TURPLE compared with state-of-the-art approaches. Code is available at https://anonymous.4open.science/r/TURPLE-634F.