Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss
Journal article
Cui, Zhesen, Ding, Zhe, Xu, Jing, Zhang, Shaotong, Wu, Jinran and Lian, Wei. (2024). Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss. Scientific Reports. 14(1), p. Article 13601. https://doi.org/10.1038/s41598-024-63878-z
Authors | Cui, Zhesen, Ding, Zhe, Xu, Jing, Zhang, Shaotong, Wu, Jinran and Lian, Wei |
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Abstract | Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l_2-norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods. |
Year | 2024 |
Journal | Scientific Reports |
Journal citation | 14 (1), p. Article 13601 |
Publisher | Nature Publishing Group |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-024-63878-z |
PubMed ID | 38867068 |
Scopus EID | 2-s2.0-85195899702 |
PubMed Central ID | PMC11169250 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 1-16 |
Funder | Chunhui Program Collaborative Scientific Research Project |
Fundamental Research Program of Shanxi Province, China | |
National Natural Science Foundation of China (NSFC) | |
Belt and Road Special Foundation of The National Key Laboratory of Water Disaster Prevention | |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 13 Jun 2024 |
Publication process dates | |
Accepted | 03 Jun 2024 |
Deposited | 20 Jan 2025 |
Grant ID | 202202004 |
202303021222271 | |
905986 | |
42276215 | |
2021491811 | |
Additional information | © The Author(s) 2024 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
https://acuresearchbank.acu.edu.au/item/91292/probabilistic-sunspot-predictions-with-a-gated-recurrent-units-based-combined-model-guided-by-pinball-loss
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Publisher's version
OA_Cui_2024_Probabilistic_sunspot_predictions_with_a_gated.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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