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

2026-06-12 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

ProtoX-AD is a novel prototype-based, self-explainable framework for self-supervised classification-based time series anomaly detection (SSC-TSAD). It addresses the explainability gap in existing SSC-TSAD methods by learning transformation-aware latent representations and interpretable prototypes, enabling both accurate anomaly detection and distinct anomalous profile characterization. Experiments on synthetic (UMD) and real-world (Global Temperature Anomalies, Yorkshire Water Leak Detection) datasets show ProtoX-AD achieves detection performance comparable to black-box counterparts. With manually designed transformations, it outperforms the explainable baseline KMEx and provides more consistent, semantically meaningful explanations, achieving lower explanation errors (MAE, MSE). However, learnable neural transformations consistently degrade performance and explanation quality.

Key takeaway

For Machine Learning Engineers developing time series anomaly detection systems, ProtoX-AD offers a robust approach to integrate explainability without sacrificing performance. You should prioritize manually designed transformations that encode domain knowledge about anomaly structures, as this significantly improves both detection accuracy and the semantic meaningfulness of explanations. Consider ProtoX-AD to characterize distinct anomalous profiles, moving beyond simple anomaly scores to provide actionable insights.

Key insights

ProtoX-AD integrates interpretable prototypes into self-supervised time series anomaly detection for explainable results and anomaly characterization.

Principles

• Domain knowledge in transformation design is crucial for effective TSAD and interpretability.
• Self-explainability does not inherently degrade anomaly detection performance.
• Consistent latent-input alignment is key for trustworthy prototype-based explanations.

Method

ProtoX-AD's pipeline includes transformation, feature extraction, dual reconstruction, prototype, and classification modules. It trains with classification, dual reconstruction, and meaningful prototype learning objectives, using cross-entropy for anomaly scoring.

In practice

• Use manually designed transformations aligned with anomaly structure for superior TSAD performance.
• Employ prototype-based models to characterize distinct anomalous profiles.

Topics

• Time Series Anomaly Detection
• Explainable AI
• Self-Supervised Learning
• Prototype Learning
• Anomaly Characterization
• Transformation Design

Code references

• Aitorzan3/ProtoX-AD

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.