Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement

Abstract The F10.7 index is crucial for assessing solar activity, significantly impacting communication, navigation, and satellite operations. The intrinsic complexity and variability of solar activity often result in sudden perturbations in the F10.7 index, compromising the accuracy and stability of forecasts. To address this challenge, we propose a novel prediction strategy that separately forecasts fundamental trends driven by the medium-to-long-term evolution of the solar cycle and the 27 day rotational modulation, along with transient disturbances caused by solar flares and the rapid evolution of active regions. These forecasts are then integrated to enhance overall prediction accuracy. We incorporate additional features such as the soft X-ray flare index (FI SXR ), magnetic type of the active region (new_Mag), and X-ray background flux (XBF) to enhance the understanding of the underlying physical processes of solar activity. Our experiments, conducted using advanced forecasting models on the SG-F10.7-All data set, validate the efficacy of our proposed strategy. Notably, the iTransformer model demonstrates superior performance in both short-term and medium-term forecasting scenarios. The inclusion of FI SXR , new_Mag, and XBF significantly improves forecasting accuracy, highlighting their importance in improving the F10.7 index predictions. Our method outperforms international models from the Space Weather Prediction Center, British Geological Survey, and Collecte Localisation Satellites, exhibiting greater accuracy and adaptability across various solar activity phases. This finding provides a novel approach for precise forecasting of the F10.7 index.