Understanding the Impact of Statistical Time Series Features for Flare Prediction Analysis

Machine learning-based space weather analytics has attracted much attention due to the potential damages that can be caused by the extreme space weather events. Using a recently released data benchmark, named SWAN-SF, designed for solar flare forecasting based on the pre-flare time series of solar magnetic field parameters, we conduct a case study on the impacts of statistical features derived from the multivariate time series. We investigate the relationship between the number of needed statistical features extracted from the multi-variate time series and the performance of flare forecast models. To that end, we employ random forest and mean decrease impurity to determine a feature selection methodology along with an evaluation procedure. The proposed evaluation method delivers a balance between the two frequently used metrics in this domain, namely True Skill Statistic and Heidke Skill Score. Our approach allows to introduce a generic feature selection and evaluation procedure that is independent from the minor and often obscured decisions that must be made for having a binary forecast model, while presenting interpretable and actionable tools that can help non-data experts make more informed and realistic decisions.