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

• URL: http://arxiv.org/abs/2108.01407v1
• Date: Tue, 3 Aug 2021 10:45:20 GMT
• Title: GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
• Authors: Ana Kostovska, Matej Petkovic\'c, Toma\v{z} Stepi\v{s}nik, Luke Lucas, Timothy Finn, Jos\'e Mart\'inez-Heras, Pan\v{c}e Panov, Sa\v{s}o D\v{z}eroski, Alessandro Donati, Nikola Simidjievski, Dragi Kocev
• Abstract summary: GalaxAI is a versatile machine learning toolbox for analysis of spacecraft telemetry data. It employs various machine learning algorithms for multivariate time series analyses, classification, regression and structured output prediction. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft.
• Score: 48.42042893355919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, regression and structured output prediction, capable of handling high-throughput heterogeneous data. These methods allow for the construction of robust and accurate predictive models, that are in turn applied to different tasks of spacecraft monitoring and operations planning. More importantly, besides the accurate building of models, GalaxAI implements a visualisation layer, providing mission specialists and operators with a full, detailed and interpretable view of the data analysis process. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft: i) analysis and planning of Mars Express thermal power consumption and ii) predicting of INTEGRAL's crossings through Van Allen belts.

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