Explainable Sequential Anomaly Detection via Prototypes

Abstract

We propose EASD, a prototype-based framework for explainable sequential anomaly detection. By linking anomalous subsequences to representative prototypes, EASD provides human-understandable explanations while maintaining strong detection accuracy.