A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

Yuchen Dang, Ziqi Chen, Heng Li, Hai Shu

Abstract:

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their ensemble models in forecasting sunspot numbers. Our proposed ensemble model XGBoost-DL, which uses XGBoost to ensemble the deep learning models,
achieves the best forecasting performance among all considered models and the NASA’s forecast. Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 and 164.62 in November 2035, similar to but later than the NASA’s at 137.7 in October 2024 and 161.2 in December 2034.