ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation-specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they provide limited insight into the characteristics of flagged anomalies. To address this limitation, we propose ProtoX-AD, a prototype-based self-explainable framework for self-supervised TSAD. ProtoX-AD learns transformation-aware latent representations ...
| Authors | Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm et al. |
|---|---|
| Primary category | stat.ML |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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