  Over the past few decades, many applications of physics-based simulations and
data-driven techniques (including machine learning and deep learning) have
emerged to analyze and predict solar flares. These approaches are pivotal in
understanding the dynamics of solar flares, primarily aiming to forecast these
events and minimize potential risks they may pose to Earth. Although current
methods have made significant progress, there are still limitations to these
data-driven approaches. One prominent drawback is the lack of consideration for
the temporal evolution characteristics in the active regions from which these
flares originate. This oversight hinders the ability of these methods to grasp
the relationships between high-dimensional active region features, thereby
limiting their usability in operations. This study centers on the development
of interpretable classifiers for multivariate time series and the demonstration
of a novel feature ranking method with sliding window-based sub-interval
ranking. The primary contribution of our work is to bridge the gap between
complex, less understandable black-box models used for high-dimensional data
and the exploration of relevant sub-intervals from multivariate time series,
specifically in the context of solar flare forecasting. Our findings
demonstrate that our sliding-window time series forest classifier performs
effectively in solar flare prediction (with a True Skill Statistic of over
85\%) while also pinpointing the most crucial features and sub-intervals for a
given learning task.
