European Space Agency Dataset and Benchmark for Anomaly Detection in Real-World Time Series

Krzysztof Kotowski, Christoph Haskamp, Jacek Andrzejewski, Bogdan Ruszczak, Jakub Nalepa, Daniel Lakey, Peter Collins, Jose Martinez Heras, Gabriele De Canio

Keywords: time series, anomaly detection, real data, benchmark, space operations

TL;DR: The paper describes a dataset and benchmark for anomaly detection in real-world time series data from European Space Agency spacecraft

Abstract: Time series from spacecraft sensors are high-dimensional, nonstationary, nonlinear, irregularly sampled, and exhibit both spatial and temporal dependencies. Detecting anomalies in such signals is critical for both on-ground and in-orbit space operations. The potential of machine learning in this task is currently hampered by a lack of comprehensive datasets and benchmarks that capture its real-world complexity. The European Space Agency Benchmark for Anomaly Detection (ESA-ADB) addresses this issue and establishes a new standard in the domain. It is a result of close cooperation between engineers from the European Space Operations Center and machine learning experts from industry and academia. Our newly introduced dataset (zenodo.org/records/15237121) contains several years of real-life raw data from 3 large spacecraft, including 224 channels, 821 control signals, and 1430 annotated events, which makes it the biggest dataset of its kind in the literature. The associated benchmark defines 9 specific requirements and 5 evaluation metrics for assessing anomaly detection algorithms in operational practice. The results indicate that widely used anomaly detection algorithms, even with our proposed adaptations, are not yet suitable for effective deployment. Thus, ESA-ADB remains an open challenge, being further explored through a dedicated Kaggle competition (kaggle.com/competitions/esa-adb-challenge).

Dataset URL: https://zenodo.org/records/15237121

Code URL: https://github.com/kplabs-pl/ESA-ADB

Primary Area: Other (please use sparingly, only use the keyword field for more details)

Submission Number: 994