中文版 | English
Title

A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction

Author
Corresponding AuthorShu, Hai
Publication Years
2022-12-31
DOI
Source Title
ISSN
0883-9514
EISSN
1087-6545
Volume36Issue:1
Abstract
Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82 ) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51 ), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35 ) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45 ). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at .https://github.com/yd1008/ts_ensemble_sunspot.
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000800024800001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttps://kc.sustech.edu.cn/handle/2SGJ60CL/335403
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.NYU, Ctr Data Sci, New York, NY USA
2.East China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai, Peoples R China
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
4.NYU, Dept Biostat, Sch Global Publ Hlth, New York, NY 10012 USA
Recommended Citation
GB/T 7714
Dang, Yuchen,Chen, Ziqi,Li, Heng,et al. A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction[J]. APPLIED ARTIFICIAL INTELLIGENCE,2022,36(1).
APA
Dang, Yuchen,Chen, Ziqi,Li, Heng,&Shu, Hai.(2022).A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction.APPLIED ARTIFICIAL INTELLIGENCE,36(1).
MLA
Dang, Yuchen,et al."A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction".APPLIED ARTIFICIAL INTELLIGENCE 36.1(2022).
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