Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network
Abstract
Purpose
To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights.
Design/methodology/approach
Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established.
Findings
Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods.
Originality/value
SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.
Keywords
Acknowledgements
This work is supported by National Social Science Fund of China (Grant No. 14BJY139), National Natural Science Foundation of China (Grant No. 71402048) and Fund of Research Center of Hubei Logistics Development.
Conflict of interests: The authors declare that there is no conflict of interests regarding the publication of this paper.
Citation
Cheng, Z. and Juncheng, T. (2015), "Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network", Kybernetes, Vol. 44 No. 4, pp. 646-666. https://doi.org/10.1108/K-09-2014-0201
Publisher
:Emerald Group Publishing Limited
Copyright © 2015, Emerald Group Publishing Limited