提出可解释的时间序列异常检测框架，能自动识别异常特征并给出合理解释。
ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
- 基于原型学习，结合变换感知表征实现自监督检测。
- 在真实与合成数据上性能媲美黑箱模型，解释更一致且语义清晰。
- 适合需要理解异常成因的工业监控、金融风控等场景。
近年来，时间序列异常检测（TSAD）发展出基于自监督分类的方法，通过对正常样本施加变换，训练分类器识别特定变换模式，从而通过分类错误率提升来发现异常。尽管性能优异，这类方法缺乏可解释性，难以揭示异常的具体特征。为此，我们提出ProtoX-AD——一种基于原型的自解释时间序列异常检测框架。该框架在学习变换感知的隐空间表示的同时，构建可解释的原型，使模型不仅能准确检测异常，还能通过原型匹配识别出不同的异常类型。此外，它支持系统分析不同变换设计对检测性能和可解释性的影响。在合成与真实世界数据集上的实验表明，ProtoX-AD的检测性能与黑箱方法相当，且生成的解释更加一致且具有语义意义。代码已公开于https://github.com/Aitorzan3/ProtoX-AD。
原文摘要 · Abstract (English)
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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