Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

ArXi:2604.17616v1 Announce Type: new Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states.