A TimesNet-based Real-time Forecasting System for the F10.7 Index Using DRAO and Chinese Langfang Datasets
文献类型：期刊论文
| 作者 | Li, Xuebao1; Ji, Xiaojia1; Zheng, Yanfang1; Wu, Zixian1; Ma, Xuran1; Wei, Jinfang1,7; Dong L(董亮)6; Abidin, Zamri Zainal4,5; Yan, Shuainan3; Ye, Hongwei1 |
| 刊名 | ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
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| 出版日期 | 2026-02-01 |
| 卷号 | 282期号:2 |
| ISSN号 | 0067-0049 |
| DOI | 10.3847/1538-4365/ae2be3 |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | In this paper, we construct the DRAO univariate dataset, the DRAO multivariate dataset, and the Chinese Langfang dataset. We develop and compare five deep learning models (TimesNet, iTransformer, PatchTST, N-Beats, BiGRU) and a benchmark artificial neural network (ANN) model to predict the F10.7 index. We study the impact of different feature combinations on the performance of the recommended TimesNet model. Furthermore, we develop a real-time forecasting system for the F10.7 index, incorporating both univariate and multivariate TimesNet models. During the same period, we compare F10.7 prediction performance between our system and that of four foreign institutions (British Geological Survey (BGS), SWPC, Collecte Localisation Satellites (CLS), DRAO). We conduct daily averaged and hourly resolution forecasting using the Langfang dataset. To our knowledge, we establish the first TimesNet-based framework for F10.7 prediction, advancing hourly resolution F10.7 forecasting for the first time. The main results are as follows. (1) The univariate TimesNet model achieves superior prediction performance on the first to the 27th day of forecasting, outperforming both four deep learning models and the ANN model. With the increase in the prediction days, the prediction performance of the six models all shows a downward trend. (2) The multivariate TimesNet-FIAC model, using optimal feature combinations, outperforms the univariate TimesNet-F model. (3) In short-term prediction, TimesNet-FIAC within our system surpasses four foreign institutions. On the first forecasting day, its root mean square error, mean absolute error, and mean absolute percentage error decrease by 15.06%, 18.54%, and 20.90% compared to BGS, and by 3.54%, 10.21%, and 14.94% compared to CLS. (4) On the Langfang dataset, TimesNet-F demonstrates superior generalization in daily averaged short-term forecasting, and maintains good and stable performance in hourly resolution short-term prediction. |
| 学科主题 | 天文学 ; 射电天文学 ; 天文学其他学科 |
| URL标识 | 查看原文 |
| 出版地 | No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND |
| 资助项目 | Jiangsu Province Natural Science Foundation[BK20241830]; National Natural Science Foundation of China[12473056] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001673208200001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | Jiangsu Province Natural Science Foundation[BK20241830] ; National Natural Science Foundation of China[12473056] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/28898]  |
| 专题 | 云南天文台_射电天文研究组 |
| 通讯作者 | Zheng, Yanfang |
| 作者单位 | 1.School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, People’s Republic of China; zyf062856@163.com; 2.Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia 3.State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; 4.National Centre for Particle Physics, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; 5.Radio Cosmology Lab, Centre for Astronomy and Astrophysics Research, Department of Physics, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; 6.Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming 650216, People’s Republic of China; 7.School of Software, Southeast University, Nanjing, People’s Republic of China; |
| 推荐引用方式 GB/T 7714 | Li, Xuebao,Ji, Xiaojia,Zheng, Yanfang,et al. A TimesNet-based Real-time Forecasting System for the F10.7 Index Using DRAO and Chinese Langfang Datasets[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2026,282(2). |
| APA | Li, Xuebao.,Ji, Xiaojia.,Zheng, Yanfang.,Wu, Zixian.,Ma, Xuran.,...&Noordin, K.A..(2026).A TimesNet-based Real-time Forecasting System for the F10.7 Index Using DRAO and Chinese Langfang Datasets.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,282(2). |
| MLA | Li, Xuebao,et al."A TimesNet-based Real-time Forecasting System for the F10.7 Index Using DRAO and Chinese Langfang Datasets".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 282.2(2026). |
入库方式： OAI收割
来源：云南天文台
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