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.
@article{arxiv.2605.06681,
title = {A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry},
author = {Lorenzo Riccardo Allegrini and Geremia Pompei},
journal= {arXiv preprint arXiv:2605.06681},
year = {2026}
}
Comments
15 pages, 3 figures, 1 table. Submitted to the ML4ITS workshop at the ECML PKDD 2025 conference. Awarded 2nd place in the final round of the Spacecraft Anomaly Challenge on ESA dataset. (Ranked 1st on the Kaggle public leaderboard and 3rd on the private leaderboard)