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 Zhang

ABSTRACT

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 Algorithm

CITE 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

[1] J. Yuan, Y. Gao, B. Xie, H. Li, and W. Jiang, "Prediction method of photovoltaic power based on combination of CEEMDAN-SSA-DBN and LSTM," Science and Technology for Energy Transition (STET), vol. 78, 20231.
[2] B. Chen, J. Liu, Z. Ruan, M. Yue, H. Long, and W. Yao, "Freight traffic of civil aviation volume forecast based on hybrid ARIMA-LR model," in International Conference on Smart Transportation and City Engineering (STCE 2022), M. Mikusova, Ed., Chongqing, China: SPIE, Dec. 2022.
[3] S. S. R. Moustafa and S. S. Khodairy, "Comparison of different predictive models and their effectiveness in sunspot number prediction," Physica Scripta, vol. 98, no. 4, 2023, doi: 10.1088/1402-4896/acc21a.
[4] Y. Dang, Z. Chen, H. Li, and H. Shu, "A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction," Applied Artificial Intelligence, vol. 36, no. 1, 2022.
[5] A. Tabassum, M. Rabbani, and S. B. Omar, "An approach to study on ma, es, ar for sunspot number (sn) prediction and to forecast sn with seasonal variations along with trend component of time series analysis using moving average (ma) and exponential smoothing (es)," in 1st International Conference on Advances in Electrical and Computer Technologies, ICAECT 2019, April 26, 2019  -  April 27, 2019, in Lecture Notes in Electrical Engineering, vol. 672. Coimbatore, India: Springer Science and Business Media Deutschland GmbH, 2020, pp. 373–380.

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