Solar Cycle 25 Prediction Using an Optimized Long Short-Term Memory Mode with F10.7

Abstract

In this paper, an optimized long short-term memory (LSTM) model is proposed to deal with the smoothed monthly F_{10.7} data, aiming to predict the peak amplitude of F_{10.7} and the occurring time for Solar Cycle 25 (SC-25) to obtain the maximum amplitude of sunspot number (SSN) and the reaching time. The “re-prediction” process in the model uses the latest prediction results obtained from the previous prediction as the input for the next prediction calculation. The prediction errors between the predicted and observed peak amplitude of F_{10.7} for SC-23 and SC-24 are 2.87% and 1.09%, respectively. The predicted peak amplitude of F_{10.7} for SC-25 is 156.3, and the maximum value of SSN is calculated as 147.9, which implies that SC-25 will be stronger than SC-24. SC-25 will reach its peak in July 2025.