Forecast of Major Solar X-Ray Flare Flux Profiles Using Novel Deep Learning Models

Abstract

Abstract In this Letter, we present the application of a couple of novel deep learning models to the forecast of major solar X-ray flare flux profiles. These models are based on a sequence-to-sequence framework using long short-term memory cell and an attention mechanism. For this, we use Geostationary Operational Environmental Satellite 10 X-ray flux data from 1998 August to 2006 April. Seven hundred sixty events are used for training and 85 for testing. The models forecast 30 minutes of X-ray flux profiles during the rise phase of the solar flare with a minute time cadence. We evaluate the models using the 10-fold cross-validation and rms error (RMSE) based on flux profiles and RMSE based on its peak flux. For comparison we consider two simple deep learning models and four conventional regression models. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of solar flare X-ray flux profiles, without any preprocessing to extract features from data. Second, our proposed models outperform the other models. Third, our models achieve better performance for forecasting X-ray flux profiles with low-peak fluxes than those with high-peak fluxes. Fourth, our models successfully predict flare duration with high correlations for both all cases and cases at peak times. Our study indicates that our deep learning models can be useful for forecasting time-series data in astronomy and space weather, even for impulsive events such as major flares.