Dual-stage Flare Prediction Using a Full-disk Flare Index: Trend-disturbance Decomposition and Hybrid Forecasting Model

Abstract Accurate prediction of solar flares is essential for space weather warnings and safeguarding technological infrastructure. This study proposes a dual-stage flare prediction framework leveraging the full-disk flare index (FI). In the first stage, FI is decomposed into long-term trend and short-term disturbance components via a “trend-disturbance” decomposition strategy. The iTransformer model independently forecasts each component, which is then fused to generate high-fidelity FI predictions. The second stage develops a regression-classification architecture that maps predicted FI values to flare intensity levels, enabling comprehensive full-disk flare forecasting. Experimental results indicate that the decomposition strategy improves performance across all flare classes, reducing the mean absolute error of FI prediction to 19.066 and achieving a TSS of 0.639 and an F1 score of 0.649 for M-class flares. Tested from the solar minimum in 2019 through the 2024 solar maximum, the framework surpasses the operational forecasting capabilities of leading space weather prediction centers such as SWPC and SEPC, successfully delivering reliable 72 hr flare warnings during 2024 May events. The dual-stage framework introduces an innovative approach to improving the accuracy and reliability of flare prediction, offering a valuable reference for forecasting extreme solar activity events.