Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach

In total, this dataset comprises 244 strong SEP events that clearly exceed the threshold of 10 pfu in the GOES P3 channel and 189 weak events observed in near-Earth space from 1986 to 2018. Additionally, the dataset includes time-series slices of GOES proton and X-ray fluxes for all the events, where each slice consists of a 12-hour observation window prior to the event onset time, and the peak flux period of events. A detailed description of dataset generation and available parameters can be found in [29]. B. Experimental Settings In supervised classification tasks, datasets with labeled samples are commonly divided into distinct subsets with knowledge of the included labels [8]. The extracted features are used to configure the parameters of the chosen algorithm in the training set, and the classifier's predictive performance on new data is determined using the testing set. Given our prediction task as a classification problem, we partition our dataset into two non-overlapping subsets: a training set (i.e., 996 samples) and a testing set (i.e., 922 samples). Similar but extending to the forecasting approach in [30], we explore the model capabilities for different short-term prediction windows of 6, 8, and 10 hours, as well as lag windows of 5, 15, 30, 45, 60, 120, and 180 minutes.