Grey Wolf optimization supported vector regression prediction model based on variational mode decomposition

Jianbo Wu; Yujun Liu; Xian Du; Xiaolei Xu; Jiqiang Ge; Yusen Chen

doi:10.1117/12.3029288

6 May 2024 Grey Wolf optimization supported vector regression prediction model based on variational mode decomposition

Jianbo Wu, Yujun Liu, Xian Du, Xiaolei Xu, Jiqiang Ge, Yusen Chen

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131073S (2024) https://doi.org/10.1117/12.3029288
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

This paper innovatively constructs a Grey Wolf optimized support vector regression model (VMD-GGO-SVR) based on variational mode decomposition to deal with the complexity of traffic flow data, which model realizes efficient complex prediction in multidimensional data processing. Firstly, the vehicle flow data is decomposed by variational mode decomposition (VMD), and then the support vector regression model (GWO-SVR) is constructed by Grey Wolf optimization algorithm. The experimental results show that the VMD-GGO-SVR model has improved by 73.89% in the goodness of fit R index, and performs well in both the training set and the test set, which proves that it has a significant effect in optimization. Through its own classification function, the model splits and solves the complex data with characteristics, and combines them into the final prediction result.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianbo Wu, Yujun Liu, Xian Du, Xiaolei Xu, Jiqiang Ge, and Yusen Chen "Grey Wolf optimization supported vector regression prediction model based on variational mode decomposition", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131073S (6 May 2024); https://doi.org/10.1117/12.3029288

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