Academic Journal of Computing & Information Science, 2023, 6(13); doi: 10.25236/AJCIS.2023.061325.
Yilin Jiang, Yufei Xiong
Southwest University of Political Science and Law, Chongqing, China
This paper addresses the prediction of sunspot activity using cycle feature analysis and Long Short-Term Memory Network (LSTM) models, including LSTM-NN. Analysis of sunspot data since 1700 reveals a 100-year major cycle and an 11-year minor cycle, with findings indicating the current 25th solar cycle began in December 2019 and ends in January 2031, and the 26th cycle from January 2031 to January 2041, both resembling the low activity levels of the 24th cycle. Employing a multivariate construction method, the study combines LSTM with univariate and multivariate predictions in single and multi-time steps, with the optimal strategy determined by the minimum root-mean-square error. The multivariable model predicts the peak of the next cycle in November 2035, lasting until May 2037, a duration of 1 year and 6 months.
Sunspot Prediction, Cycle Feature Analysis, Long Short-Term Memory Network (LSTM), Multivariate Model
Yilin Jiang, Yufei Xiong. Prediction of Sunspot Activity Cycle Based on Long Short-Term Memory (LSTM) Network Models. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 178-185. https://doi.org/10.25236/AJCIS.2023.061325.
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