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Solar cycle prediction using a combinatorial deep learning model

文献类型：期刊论文

作者Su, Xu1,2; Liang, Bo2; Feng, Song1,2; Cai YF(蔡云芳)1,3; Dai, Wei2; Yang, Yunfei2
刊名MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
出版日期2023-11-27
卷号527期号:3页码:5675-5682
ISSN号0035-8711
关键词methods: data analysis Sun: activity
DOI10.1093/mnras/stad3451
产权排序第3完成单位
文献子类Article
英文摘要The long-term prediction of the solar cycle is of great significance for aerospace, communication, and space missions. For a long time, many studies have used relatively primitive deep learning methods to predict the solar cycle, and most of them do not perform well in the long-term prediction. In this paper, we proposed XG-SN ensemble model. The model used extreme gradient boosting (XGBoost) ensemble learning method, combined with sample convolution and interaction net (SCINet), and neural basis expansion analysis for the interpretable time series (N-BEATS) to make predictions for known solar cycles. 13 months of smoothed monthly total sunspot numbers were selected as the data set. The model performance was evaluated by mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute time lag (MATL) between the predicted and actual values. The first two evaluation metrics measured the prediction deviation from the numerical dimension, and the last one measured the prediction deviation from the temporal dimension. The results show that the model achieves the MAE, RMSE, and MATL values of 13.19, 17.13, and 0.08, respectively, in Solar Cycle 13 to 24. Our model is able to better predict in most cycles, ensuring accurate prediction of peaks with little time lag.
学科主题天文学
URL标识查看原文
出版地GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
WOS关键词NEURAL-NETWORK
资助项目National Natural Science Foundation of China[12063003]; National Natural Science Foundation of China[YNSPCC202214]; Yunnan Key Laboratory of the Solar Physics and Space Science
WOS研究方向Astronomy & Astrophysics
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:001116922000027
资助机构National Natural Science Foundation of China[12063003] ; National Natural Science Foundation of China[YNSPCC202214] ; Yunnan Key Laboratory of the Solar Physics and Space Science
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/26532]  
专题云南天文台_抚仙湖太阳观测站
作者单位1.Yunnan Key Laboratory of the Solar physics and Space Science, Kunming 650216, China;
2.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;
3.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
推荐引用方式
GB/T 7714		 Su, Xu,Liang, Bo,Feng, Song,et al. Solar cycle prediction using a combinatorial deep learning model[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2023,527(3):5675-5682.
APA Su, Xu,Liang, Bo,Feng, Song,蔡云芳,Dai, Wei,&Yang, Yunfei.(2023).Solar cycle prediction using a combinatorial deep learning model.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,527(3),5675-5682.
MLA Su, Xu,et al."Solar cycle prediction using a combinatorial deep learning model".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 527.3(2023):5675-5682.

入库方式： OAI收割

来源：云南天文台

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