A Comparative Study of Traditional Methods and Hybridization for Predicting Non-Stationary Sunspot Time Series

Predicting sunspot numbers presents ongoing challenges in fore-casting, including non-stationary patterns and unclear fluctuation dynamics. This study compares traditional methods and hybrid models, incorporating machine learning techniques, to predict monthly mean sunspot numbers (MMSNs) from January 1, 1900, to December 31, 2022. Among the traditional methods, ARIMA(5,0,4) demonstrated performance with an MSE of 580.949, RMSE of 24.103, MAE of 17.19, and MAPE of 0.511. However, the proposed hybrid model, which com-bines ARIMA(5,0,4) with additive regression (AR) using Regression by Discretization (RegbyDisc) based on J48, achieved markedly superior forecasting accuracy with an MSE of 114.653, RMSE of 10.708, MAE of 6.441, and MAPE of 0.438. We employed this hybrid model to forecast sunspot numbers from January 2023 to December 2025.