A novel grey prediction model with structure variability and real domain fractional order and its performance comparisons
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 6 January 2023
Issue publication date: 16 March 2023
Abstract
Purpose
This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.
Design/methodology/approach
Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.
Findings
The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.
Originality/value
A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.
Highlights
The highlights of the paper are as follows:
A new grey prediction model with a unified nonlinear structure is proposed.
The new model can be fully compatible with multiple traditional grey models.
The new model solves the defect of poor adaptability of the traditional grey models.
The parameters of the new model are optimized by PSO algorithm.
Cases verify that the new model outperforms other models significantly.
Keywords
Acknowledgements
Funding: The National Natural Science Foundation of China (Grant Nos. 72071023, 71771033) and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant Nos. KJZD-K202000804; KJZD-K20220210).
Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Mao, C., Gou, X. and Zeng, B. (2023), "A novel grey prediction model with structure variability and real domain fractional order and its performance comparisons", Grey Systems: Theory and Application, Vol. 13 No. 2, pp. 381-405. https://doi.org/10.1108/GS-07-2022-0072
Publisher
:Emerald Publishing Limited
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