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We present GalaxAI - a versatile machine learning toolbox for efficient andinterpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employsvarious machine learning algorithms for multivariate time series analyses,classification, regression and structured output prediction, capable ofhandling high-throughput heterogeneous data. These methods allow for theconstruction of robust and accurate predictive models, that are in turn appliedto different tasks of spacecraft monitoring and operations planning. Moreimportantly, besides the accurate building of models, GalaxAI implements avisualisation layer, providing mission specialists and operators with a full,detailed and interpretable view of the data analysis process. We show theutility and versatility of GalaxAI on two use-cases concerning two differentspacecraft: i) analysis and planning of Mars Express thermal power consumptionand ii) predicting of INTEGRAL's crossings through Van Allen belts.

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data