Solar cycle prediction using a combinatorial deep learning model

doi:10.1093/mnras/stad3451

	Solar cycle prediction using a combinatorial deep learning model
	Su, Xu 1,3; Liang, Bo 3; Feng, Song 1,3; Cai YF(蔡云芳)1,2; Dai, Wei 3; Yang, Yunfei 3
刊名	MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
	2023-11-27
卷号	527 期号:3 页码:5675-5682
关键词	methods: data analysis Sun: activity
ISSN号	0035-8711
DOI	10.1093/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
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关键词	NEURAL-NETWORK
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
内容类型	期刊论文
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.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China 3.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, 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.