| Solar cycle prediction using a combinatorial deep learning model | |
Su, Xu1,2; Liang, Bo1; Feng, Song1,2; Cai YF(蔡云芳)2,3 ; Dai, Wei1; Yang, Yunfei1
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| 发表期刊 | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
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| 2023-11-27 | |
| 卷号 | 527期号:3页码:5675-5682 |
| DOI | 10.1093/mnras/stad3451 |
| 产权排序 | 第3完成单位 |
| 收录类别 | SCI ; EI |
| 关键词 | methods: data analysis Sun: activity |
| 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. |
| 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 |
| 项目资助者 | National Natural Science Foundation of China[12063003] ; National Natural Science Foundation of China[YNSPCC202214] ; Yunnan Key Laboratory of the Solar Physics and Space Science |
| 语种 | 英语 |
| 学科领域 | 天文学 |
| 文章类型 | Article |
| 出版者 | OXFORD UNIV PRESS |
| 出版地 | GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND |
| ISSN | 0035-8711 |
| URL | 查看原文 |
| WOS记录号 | WOS:001116922000027 |
| WOS研究方向 | Astronomy & Astrophysics |
| WOS类目 | Astronomy & Astrophysics |
| 关键词[WOS] | NEURAL-NETWORK |
| EI入藏号 | 20235115233702 |
| EI主题词 | Forecasting |
| EI分类号 | 461.4 Ergonomics and Human Factors Engineering - 657.1 Solar Energy and Phenomena - 922.2 Mathematical Statistics |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 版本 | 出版稿 |
| 条目标识符 | http://ir.ynao.ac.cn/handle/114a53/26532 |
| 专题 | 抚仙湖太阳观测和研究基地 |
| 作者单位 | 1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; 2.Yunnan Key Laboratory of the Solar physics and Space Science, Kunming 650216, 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. |
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| Solar cycle predicti（1683KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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