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.

本研究的主要贡献在于发展了适用于多变量时间序列的可解释分类器，并展示了一种基于滑动窗口子区间排序的新型特征排序方法，通过在太阳耀斑预测的背景下，填补了用于高维数据的复杂且不易理解的黑盒模型与多元时间序列相关子区间的探索之间的差距。研究结果表明，滑动窗口时间序列森林分类器在太阳耀斑预测方面表现出较高的准确性（超过85%的真实技巧统计值），同时可以确定给定学习任务中最关键的特征和子区间。

基于滑动窗口多变量时间序列随机森林分类器的活动区域闪耀预测