  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.
