  The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time
series benchmark dataset recently created to serve the heliophysics community
as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique
features, with 24 quantitative features computed from the photospheric magnetic
field maps of active regions, describing their precedent flare activity. In
this study, for the first time, we systematically attacked the problem of
quantifying the relevance of these features to the ambitious task of flare
forecasting. We implemented an end-to-end pipeline for preprocessing, feature
selection, and evaluation phases. We incorporated 24 Feature Subset Selection
(FSS) algorithms, including multivariate and univariate, supervised and
unsupervised, wrappers and filters. We methodologically compared the results of
different FSS algorithms, both on the multivariate time series and vectorized
formats, and tested their correlation and reliability, to the extent possible,
by using the selected features for flare forecasting on unseen data, in
univariate and multivariate fashions. We concluded our investigation with a
report of the best FSS methods in terms of their top-k features, and the
analysis of the findings. We wish the reproducibility of our study and the
availability of the data allow the future attempts be comparable with our
findings and themselves.
