Sunspot activity prediction based on adaptive hybrid algorithms
DOI: 10.23977/geors.2024.070102 | Downloads: 6 | Views: 529
Author(s)
Chunxu Zhang 1, Yesheng Liang 1
Affiliation(s)
1 School of Accounting, Tianjin University of Finance and Economics, Tianjin, China
Corresponding Author
Chunxu ZhangABSTRACT
In this paper, by combining the ARIMA model and the BP neural network model, we establish an adaptive hybrid ARIMA-BP neural network model, which provides more accurate results for sunspot prediction. For solar activity prediction, in this paper, based on the multivariate nonlinear regression and BP neural network model, we utilize the differential evolutionary algorithm for model solving and obtain satisfactory hybrid model solving results. These results provide new perspectives and methods for solar activity prediction, and provide useful references and insights for research and practice in related fields.
KEYWORDS
ARIMA Model, BP Neural Network, Hybrid ARIMA-BP Neural Network Model, Multivariate Nonlinear Regression, Differential Evolutionary AlgorithmCITE THIS PAPER
Chunxu Zhang, Yesheng Liang, Sunspot activity prediction based on adaptive hybrid algorithms. Geoscience and Remote Sensing (2024) Vol. 7: 14-23. DOI: http://dx.doi.org/10.23977/geors.2024.070102.
REFERENCES
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