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

Problem
This paper addresses the gap in explainability within self-supervised classification-based time series anomaly detection (TSAD) methods. While these approaches have demonstrated strong performance by leveraging transformation-specific patterns, they often lack the ability to provide interpretable insights into the nature of detected anomalies. The authors propose ProtoX-AD as a solution to this challenge, aiming to enhance both detection accuracy and the interpretability of anomalies. This work is presented as a preprint and has not undergone peer review.

Method
ProtoX-AD employs a prototype-based framework that integrates self-supervised learning for TSAD. The architecture consists of a neural network that learns transformation-aware latent representations, which are then used to generate interpretable prototypes. These prototypes serve as reference points for identifying and characterizing anomalies. The model is trained on transformed normal samples, with the objective of minimizing classification errors associated with these transformations. The authors systematically analyze the impact of different transformation designs on both detection performance and explainability, providing a comprehensive understanding of how these factors interact. The training compute details are not explicitly disclosed, but the authors emphasize the efficiency of their approach in comparison to traditional black-box models.

Results
Experimental evaluations on both synthetic and real-world datasets reveal that ProtoX-AD achieves competitive anomaly detection performance. Specifically, it matches or exceeds the performance of existing black-box models while providing more consistent and semantically meaningful explanations. The authors report that ProtoX-AD outperforms explainable baselines in terms of explanation quality, although specific metrics and effect sizes are not detailed in the summary. The results indicate that ProtoX-AD effectively balances detection accuracy with interpretability, a critical requirement in many practical applications of TSAD.

Limitations
The authors acknowledge that while ProtoX-AD improves explainability, the framework may still be limited by the inherent complexity of the underlying data and transformations. Additionally, the reliance on prototype-based explanations may not capture all nuances of anomaly characteristics, particularly in highly variable datasets. The paper does not address potential scalability issues when applied to large-scale datasets or real-time anomaly detection scenarios, which could impact its practical deployment.

Why it matters
ProtoX-AD represents a significant advancement in the field of time series anomaly detection by bridging the gap between performance and interpretability. The ability to provide clear, prototype-based explanations for detected anomalies enhances trust and usability in critical applications such as finance, healthcare, and industrial monitoring. This work lays the groundwork for future research into explainable AI in time series analysis, encouraging further exploration of prototype-based methods. The findings and methodologies presented in this paper are relevant for researchers and practitioners aiming to develop more interpretable machine learning systems, as published in arXiv cs.LG.