Sequential Anomaly Detection with Local and Global Explanations

Abstract

We propose GLEAD, a framework for sequential anomaly detection that provides both local explanations (highlighting anomalous subsequences) and global explanations (dataset-wide patterns). This dual perspective improves transparency for practitioners while maintaining strong detection performance.