文献类型：期刊文献

英文题名：Solar Cycle Prediction Using TCN Deep Learning Model with One-Step Pattern

作者：Zhao, Cui[1]; Liu, Kun[1]; Yang, Shangbin[2]; Xia, Jinchao[3]; Chen, Jingxia[1]; Ren, Jie[1]; Liu, Shiyuan[1]; He, Fangyuan[1]

第一作者：Zhao, Cui

机构：[1] College of Applied Science and Technology, Beijing Union University, Beijing, 102200, China; [2] National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing, 100012, China; [3] Inspur Yunzhou industrial internet Co., Ltd, No.1036, No.1036, inspur Road, High-tech Zone, Shandong Province, Jinan City, China

第一机构：北京联合大学应用科技学院

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250121461)

语种：英文

摘要：Human living environment is influenced by intense solar activity. The solar activity exhibits periodicity and regularity. Although many deep-learning models are currently used for solar cycle prediction, most of them are based on a multi-step pattern. In this paper a solar cycle prediction method based on a one-step pattern is proposed with the TCN neural network model, in which a number of historical data are input, and only one value is predicted at a time. Through an autoregressive strategy, this predicted value is added to the input sequence to generate the next output. This process is iterated until the prediction of multiple future data. The experiments were performed on the 13-month smoothed monthly total sunspot number data sourced from WDC-SILSO. The results showed that one-step pattern fits the solar cycles from 20-25 well. The average fitting errors are MAE=1.74, RMSE=2.34. Finally, the intensity of Solar Cycle 25 was predicted with one-step pattern. The peak will occur in 2024 October with a magnitude of 135.3 and end in 2030 November. By comparing the prediction results with other methods, our method are more reasonable and better than the most methods. The codes are available on github and Zenodo. ? 2025, CC BY.