A Deep Learning Model Based on Multi-Head Attention for Long-Term Forecasting of Solar Activity

Abstract

The accurate long-term forecasting of solar activity is crucial in the current era of space explorations and in the study of planetary climate evolution. With timescales of about 11 years, these forecasts deal with the prediction of the very general features of a solar cycle such as its amplitude, peak time and period. Solar radio indices, continuously measured by a network of ground-based solar radio telescopes, are among the most commonly used descriptors to characterise the solar activity level. They can act as proxies for the strength of ionising radiations, such as solar ultraviolet and X-ray emissions, which directly affect the atmospheric density. In a preliminary comparative study of a selection of univariate deep-learning methods targeting medium-term forecasts of the F10.7 index, we noticed that the performance of all the considered models tends to degrade with increasing timescales and that this effect is smoother when a multi-attention module is included in the used neural network architecture. In this work, we present a multivariate approach based on the combination of fast iterative filtering (FIF) algorithm, long-short term memory (LSTM) network and multi-attention module, trained for the present solar cycle forecasting. Several solar radio flux time series, namely F3.2, F8, F10.7, F15, F30, are fed into the neural network to forecast the F10.7 index. The results are compared with the official solar cycle forecasting released by the Solar Cycle Prediction Panel representing NOAA, NASA and the International Space Environmental Services (ISES) to highlight possible discrepancies.

Keywords

• solar activity forecasting
• solar radio index
• deep learning
• multivariate prediction
• time series forecasting
• multi-attention