Authors: Peiyu Li (Utah State University), Omar Bahri(Utah State University), Souka ̈ına Filali Boubrahimi,(Utah State University), Shah Muhammad Hamdi(Utah State University)

Solar flare prediction has become essential in space weather research due to its potential adverse space-weather ramifications. Over recent years, a set of machine learning models on solar flare prediction have been proposed and significant improvement has been made over the previous state of the art. However, most existing research work focuses on the prediction task and ignores the interpretability behind the prediction task. In this work, we propose a post-hoc explanation method based on solar flare prediction, FAST-CF. In particular, we incorporate the nearest unlike neighbor for guiding the counterfactual search, which is fast to search for the optimal result. By focusing on a small set of important dimension substitutions, the FAST-CF method guides the perturbations to generate valid counterfactual explanations by changing only a small fraction of the time steps.