Forecasting long-term sunspot numbers using the LSTM-WGAN model - Yang, Hao; Zuo, Pingbing; Zhang, Kun; Shen, Zhenning; Zou, Zhengyang; Feng, Xueshang (Frontiers in Astronomy and Space Sciences, 2025)
The sunspot number, an indicator of solar activity, is vital for forecasting variations in solar activity and predicting disturbances of the geomagnetic field. This study proposes a hybrid model that combines Long Short-Term Memory (LSTM) with the Wasserstein Generative Adversarial Network (WGAN) for sunspot number prediction. The LSTM-WGAN model performs better than the LSTM model in forecasting long-term sunspot numbers using single-step forecasting methods. To further evaluate its effectiveness, we performed a comparative analysis, by comparing predictions of LSTM-WGAN with those provided by the European Space Agency (ESA). This analysis confirmed the accuracy and reliability of LSTM-WGAN model in predicting the sunspot numbers. In particular, our model successfully predicted that the peak of sunspot numbers during the 25th Solar Cycle is slightly higher than that during the 24th Solar Cycle, which is consistent with current observations.