Recent years have seen an exponential increase in the application of machine learning (ML) techniques to several fields, including physics and astrophysics (Nguyen et al., 2019; Zhou et al., 2021; Reiss et al., 2021; Rüdisser et al., 2022). ML-based tools provide predictions more accurate than other models due to their generalisation properties. Furthermore, the current availability of computational power makes feasible building complex and prediction-effective predictors. These are the main reasons behind the heavy application of ML algorithms, even though they usually require a huge amount of training data to be provided. This need of large training data sets may be challenging in some scenarios, but it does not constitute a limitation when ML predictors rely on observations gathered on beam experiments in high-energy physics and by long-lasting space missions, for instance. Predictive tools in general, and ML models in particular, are precious resources for space missions to achieve several goals, as time series missing data filling and pattern recognition (Villani et al., 2022).