ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization

• URL: http://arxiv.org/abs/2606.13277v1
• Date: Thu, 11 Jun 2026 12:30:40 GMT
• Title: ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
• Abstract summary: ProtoX-AD is a prototype-based self-explainable framework for self-supervised TSAD.<n>It learns transformation-aware latent representations alongside interpretable prototypes.<n>ProtoX-AD achieves detection performance comparable to its black-box counterparts.
• Score: 22.20993136241034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 alongside interpretable prototypes, enabling both accurate anomaly detection and the identification of distinct anomalous profiles through prototype-based explanations. Additionally, it allows for systematic analysis of how transformation design impacts detection performance and explainability. Experimental results on synthetic and real-world datasets demonstrate that ProtoX-AD achieves detection performance comparable to its black-box counterparts while offering more consistent and semantically meaningful explanations than existing explainable baselines. Our code is publicly available at https://github.com/Aitorzan3/ProtoX-AD.

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