Prediction of soybean price in China using QR-RBF neural network model

Type
Article

Authors
Zhang, Dongqing
Zang, Guangming
Li, Jing
Ma, Kaiping
Liu, Huan

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program

Online Publication Date
2018-09-06

Print Publication Date
2018-11

Date
2018-09-06

Abstract
As the price of soybean affects the soybean market development and food security in China, its forecasting is essential. A quantile regression-radial basis function (QR-RBF) neural network model is introduced in this paper. The model has two characteristics: (1) using quantile regression models to describe the distribution of the soybean price range; and (2) using RBF neural networks to approximate the nonlinear component of the soybean price. In order to optimize the QR-RBF neural network model parameters, a hybrid algorithm known as GDGA, based on a combination of the genetic algorithm (performing a global search) and a gradient descent method (performing a local search), is proposed in this paper. Data regarding the monthly domestic soybean price in China were analyzed and the results indicate that the proposed hybrid GDGA is effective. Furthermore, the results suggest that the influencing factors of soybean price vary at different price levels. Money supply and port distribution price of imported soybean were found to be important across a range of quantiles; output of domestic soybean and consumer confidence index were important only for low quantiles; and import volume of soybean and consumer price index were important only for high quantiles.

Citation
Zhang D, Zang G, Li J, Ma K, Liu H (2018) Prediction of soybean price in China using QR-RBF neural network model. Computers and Electronics in Agriculture 154: 10–17. Available: http://dx.doi.org/10.1016/j.compag.2018.08.016.

Publisher
Elsevier BV

Journal
Computers and Electronics in Agriculture

DOI
10.1016/j.compag.2018.08.016

Additional Links
http://www.sciencedirect.com/science/article/pii/S0168169918302199

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