Flare prediction modeling based on the time series of SHARP parameters along the polarity inversion line of active regions

The polarity inversion line (PIL) in active regions (ARs) is considered to be closely associated with solar flare eruptions. In this study, we rigorously constructed standardized data sets based on time series of different lengths using Space-weather HMI Active Region Patches (SHARP) parameters calculated along the PIL. We compared the performance of traditional non-sequential models and a time-series model in solar flare prediction tasks, as well as the predictive performance of time-series models with different input lengths within the CNN–BiLSTM–AT framework. The main findings of this study are summarized as follows: (a) SHARP parameters computed along the PIL consistently yield better prediction performance than those calculated over entire active regions. (b) In realistic and highly imbalanced prediction scenarios, the time-series model outperforms non-sequential models, achieving an F1 score of 0.59 for strong-flare prediction. (c) Robustness tests and sliding-window probability forecasts further demonstrate the practical feasibility of the proposed approach. These results provide useful guidance for data representation and model selection in solar flare forecasting.