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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.06681 |
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| _version_ | 1866909030707888128 |
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| author | Allegrini, Lorenzo Riccardo Pompei, Geremia |
| author_facet | Allegrini, Lorenzo Riccardo Pompei, Geremia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06681 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry Allegrini, Lorenzo Riccardo Pompei, Geremia Machine Learning Computer Vision and Pattern Recognition I.2.6 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. |
| title | A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry |
| topic | Machine Learning Computer Vision and Pattern Recognition I.2.6 |
| url | https://arxiv.org/abs/2605.06681 |