European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
<|reference_start|>European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry: 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.<|reference_end|>
@article{kotowski2024european,
    title={European Space Agency Benchmark for Anomaly Detection in Satellite
  Telemetry},
    author={Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan
  Ruszczak, Jakub Nalepa, Daniel Lakey, Peter Collins, Aybike Kolmas, Mauro
  Bartesaghi, Jose Martinez-Heras, Gabriele De Canio},
    journal={arXiv preprint arXiv:2406.17826},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17826},
    primaryClass={cs.LG cs.AI}
}