![[2602.20712] F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20712] F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization
Summary
This article presents a novel forecasting method for the F10.7 solar index using wavelet decomposition, demonstrating improved prediction performance compared to existing models.
Why It Matters
Accurate prediction of the F10.7 index is crucial for understanding solar activity and its impact on Earth's environment. This study introduces a new methodology that enhances forecasting capabilities, which is significant for researchers and practitioners in astrophysics and related fields.
Key Takeaways
- Wavelet decomposition significantly improves F10.7 index prediction accuracy.
- The Combination 6 method outperforms traditional models by reducing RMSE, MAE, and MAPE.
- Incorporating the International Sunspot Number does not enhance prediction performance.
- The proposed method shows superior generalization across various solar activity conditions.
- This is the first application of wavelet decomposition in F10.7 prediction.
Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2602.20712 (astro-ph) [Submitted on 24 Feb 2026] Title:F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization Authors:Xuran Ma, Xuebao Li, Yanfang Zheng, Yongshang Lv, Xiaojia Ji, Jiancheng Xu, Hongwei Ye, Zixian Wu, Shuainan Yan, Liang Dong, Zamri Zainal Abidin, Xusheng Huang, Shunhuang Zhang, Honglei Jin, Tarik Abdul Latef, Noraisyah Mohamed Shah, Mohamadariff Othman, Kamarul Ariffin Noordin View a PDF of the paper titled F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization, by Xuran Ma and 17 other authors View PDF HTML (experimental) Abstract: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. (2025) and three operational models (SWPC, BGS, CLS). Additionally, we transfer our method to the PatchTST model used in H. Ye et al. (2024) and compare our method with theirs on Dataset B. Key findings include: (1) The wavelet-base...
