[2605.06681] A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
[Submitted on 22 Apr 2026]
Title:A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
View a PDF of the paper titled A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry, by Lorenzo Riccardo Allegrini and 1 other authors
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Abstract: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.
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)
Subjects:
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.6
Cite as: arXiv:2605.06681 [cs.LG]
(or arXiv:2605.06681v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.06681
arXiv-issued DOI via DataCite
Journal reference: Communications in Computer and Information Science 2842 (2026) Chapter 7
Submission history
From: Geremia Pompei [view email] [v1] Wed, 22 Apr 2026 23:08:38 UTC (667 KB)
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