A hierarchical ensemble pipeline using shapelet and statistical features, per-channel stacking, and cross-channel aggregation with time-series cross-validation and masking achieves strong generalization on the ESA-ADB anomaly detection benchmark.
arXiv preprint arXiv:2406.17826 (2024)
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abstract
Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.
years
2026 2representative citing papers
Supervised models (especially GAT and Multiscale CNN) outperform unsupervised detectors on ESA-ADB telemetry, yet unsupervised methods deliver competitive precision at far lower compute cost.
citing papers explorer
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A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry
A hierarchical ensemble pipeline using shapelet and statistical features, per-channel stacking, and cross-channel aggregation with time-series cross-validation and masking achieves strong generalization on the ESA-ADB anomaly detection benchmark.
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Toward Deployable Satellite Anomaly Detection: A Benchmark Study on Large-Scale ESA-ADB Telemetry
Supervised models (especially GAT and Multiscale CNN) outperform unsupervised detectors on ESA-ADB telemetry, yet unsupervised methods deliver competitive precision at far lower compute cost.