A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
It addresses the practical need for robust anomaly detection in satellite telemetry for space agencies, but the approach is incremental, combining existing techniques.
The paper introduces a hierarchical ensemble pipeline for anomaly detection in ESA satellite telemetry, achieving strong generalization on the ESA-ADB benchmark by integrating shapelet-based and statistical features with multi-level modeling.
A hierarchical ensemble pipeline is introduced to address anomaly detection in multivariate telemetry data provided by European Space Agency (ESA). The method integrates shapelet-based and statistical feature extraction, per-channel modeling, intra-channel stacking, and a final cross-channel aggregation. The pipeline is trained and validated using time-series cross-validation and two-level masking strategies to prevent information leakage. Results on the European Space Agency Anomaly Detection Benchmark (ESA-ADB) challenge demonstrate strong generalization, highlighting the effectiveness of hierarchical modeling in detecting subtle anomalies in realistic satellite telemetry.