F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization
文献类型：期刊论文
| 作者 | Ma, Xuran6; Li, Xuebao6; Zheng, Yanfang6; Lv, Yongshang6; Ji, Xiaojia6; Xu, Jiancheng6; Ye, Hongwei6; Wu, Zixian6; Yan, Shuainan1; Dong L(董亮)5 |
| 刊名 | ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
 |
| 出版日期 | 2026-04-01 |
| 卷号 | 283期号:2 |
| ISSN号 | 0067-0049 |
| DOI | 10.3847/1538-4365/ae4a1a |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | In this study, we construct Dataset A for training, validation, and testing, and Dataset B to evaluate generalization. We propose a novel F10.7 index forecasting method using wavelet decomposition, which feeds F10.7 together with its decomposed approximate and detail signals into the iTransformer model. We also incorporate the International Sunspot Number (ISN) and its wavelet-decomposed signals to assess their influence on prediction performance. Our optimal method is then compared with the latest method from S. Yan et al. and three operational models (Space Weather Prediction Center, British Geological Survey, Collect Localization Satellites). Additionally, we transfer our method to the PatchTST model used in H. Ye et al. and compare our method with theirs on Dataset B. Key findings include the following: (1) The wavelet-based combination methods overall outperform the baseline using only the F10.7 index. The prediction performance improves as higher-level approximate and detail signals are incrementally added. The Combination 6 method-integrating F10.7 with its first to fifth level approximate and detail signals-outperforms methods using only approximate or detail signals. (2) Incorporating the ISN and its wavelet-decomposed signals does not enhance prediction performance. (3) The Combination 6 method significantly surpasses that of S. Yan et al. and three operational models, with rms error, mean absolute error, and mean absolute percentage error reduced by 18.22%, 15.09%, and 8.57%, respectively, against the former method. It also excels across four different conditions of solar activity. (4) Our method demonstrates superior generalization and prediction capability over the method of H. Ye et al. across all forecast horizons. To our knowledge, this is the first application of wavelet decomposition in F10.7 prediction, substantially improving forecast performance. |
| 学科主题 | 电子、通信与自动控制技术 |
| URL标识 | 查看原文 |
| 出版地 | No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND |
| WOS关键词 | SOLAR RADIO FLUX ; EMPIRICAL MODE DECOMPOSITION ; SUNSPOT NUMBER |
| 资助项目 | JST divided by Natural Science Foundation of Jiangsu Province (Jiangsu Natural Science Foundation)[BK20241830]; MOST divided by National Natural Science Foundation of China (NSFC)[12473056] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001726537000001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | JST divided by Natural Science Foundation of Jiangsu Province (Jiangsu Natural Science Foundation)[BK20241830] ; MOST divided by National Natural Science Foundation of China (NSFC)[12473056] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/29085]  |
| 专题 | 云南天文台_射电天文研究组 |
| 通讯作者 | Li, Xuebao; Zheng, Yanfang |
| 作者单位 | 1.State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; 2.Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia 3.National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur 50603, Malaysia; 4.Radio Cosmology Lab, Centre for Astronomy and Astrophysics Research, Department of Physics, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia; 5.Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming 650216, People’s Republic of China; 6.School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, People’s Republic of China; lixuebaozhengyanfang@gmail.com, zyf062856@163.com; |
| 推荐引用方式 GB/T 7714 | Ma, Xuran,Li, Xuebao,Zheng, Yanfang,et al. F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2026,283(2). |
| APA | Ma, Xuran.,Li, Xuebao.,Zheng, Yanfang.,Lv, Yongshang.,Ji, Xiaojia.,...&Noordin, Kamarul Ariffin.(2026).F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,283(2). |
| MLA | Ma, Xuran,et al."F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 283.2(2026). |
入库方式： OAI收割
来源：云南天文台
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