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.

翻译：近期时间序列异常检测（TSAD）的进展凸显了自监督分类方法的有效性。这些方法对正常训练样本施加变换，训练分类器识别特定变换模式，从而通过分类误差增大来辅助异常识别。尽管性能优异，但其缺乏可解释性构成重大挑战——对已标记异常的特征难以提供深入洞察。为此，我们提出ProtoX-AD——一种基于原型的自监督TSAD自解释框架。ProtoX-AD在感知变换的潜在表示中学习可解释原型，既能实现精准异常检测，又能通过原型解释识别差异化异常轮廓。此外，该框架支持系统分析变换设计对检测性能与可解释性的影响。在合成与真实数据集上的实验表明，ProtoX-AD在取得与黑箱方法相当的检测性能的同时，相比现有可解释基线方法能提供更一致且更具语义意义的解释。我们的代码已在https://github.com/Aitorzan3/ProtoX-AD 公开。