A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
Authors: Lorenzo Riccardo Allegrini, Geremia Pompei
Summary
The authors built a multi-stage anomaly detection system for satellite telemetry from the European Space Agency. Their pipeline extracts features from each sensor channel separately, stacks models within channels, then aggregates predictions across channels. They use careful cross-validation with masking to prevent the model from "peeking" at future data, and show strong results on ESA's anomaly detection benchmark.
Main takeaways:
- Hierarchical approach: model each sensor independently first, then combine predictions across sensors
- Two-level masking during training prevents information leakage from future timesteps
- Combines shapelet features (pattern-based) with statistical features
- Strong performance on realistic satellite data with subtle anomalies
- Time-series cross-validation ensures the system generalizes to new periods
Relevance
Not related to my persona or midtraining work — this is about satellite telemetry and time-series anomaly detection in a completely different domain.
Abstract
arXiv:2605.06681v1 Announce Type: new 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.