|Table of Contents|

Forecasting Chinese Domestic Soybean Price Based on Q-RBF Neural Network Model(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2017年01期
Page:
143-149
Research Field:
Publishing date:

Info

Title:
Forecasting Chinese Domestic Soybean Price Based on Q-RBF Neural Network Model
Author(s):
ZHANG Dong-qing; LIU Huan; ZHANG Yun-qing
（College of Engineering, Nanjing Agricultural University, Nanjing 210031, China）
Keywords:
Forecasting;  Quantile regression-radial basis function (Q-RBF) neural network;  Gradient descent method;  Genetic algorithm;  Probability density function
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2017.01.0143
Abstract:
Soybean is an important cash crop, and it is also the most important agricultural product with the highest degree of marketization and internationalization character in China. So it is essential to forecast the soybean price. A Quantile-RBF (Q-RBF) neural network model is proposed to predict Chinese domestic soybean price in this paper. The model has two characteristics as follows: (1) Quantile regression models describe the distribution over the range of the soybean price; (2) RBF neural networks approximate the nonlinear part of soybean price. The parameters of Q-RBF neural network model can be optimized through the genetic algorithm (GA) and the gradient descent method. GA is a global optimization method, however, it might be slow in convergence. On the contrary, the gradient descent method quickly converges to an optimal solution, but may converge to a local minimum or maximum and is not efficient in discontinuous problems. Therefore, an improved algorithm combining GA with gradient descent method is proposed in this paper. In the improved algorithm, the gradient descent method is used to improve the convergence efficiency of GA. The data of monthly soybean price from Jan. 2010 to Dec. 2015 were analyzed. Experimental results demonstrated that the Q-RBF neural network model and improved algorithm were accurate and effective.

References:

［1］奚晓菁. 我国大豆价格波动影响因素分析［D］. 昆明: 云南财经大学, 2013. (Xi X J. Analysis on the influencing factors of soybean price fluctuation in China［D］. Kunming: Yunnan University of Finance and Economics, 2013.)

［2］张婷. 基于ARIMA模型的国际粮食短期价格分析预测—以大豆为例［J］. 价格月刊, 2016, (470): 28-32. (Zhang T. Analysis and prediction of short term international grain price based on ARIMA model: A case study of soybean［J］. Prices Monthly, 2016, (470): 28-32.)
［3］朱婧, 范亚东, 徐勇. 基于改进GM(1,1)模型的中国大豆价格预测［J］. 大豆科学, 2016, 35(2): 315-319. (Zhu J, Fan Y D, Xu Y. Soybean price prediction in china based on modified GM(1,1) model［J］. Soybean Science, 2016, 35(2): 315-319.)
［4］程文晓. 我国大豆期货价格的预测分析［D］. 兰州: 兰州大学, 2014. (Cheng W X. Forecasting of soybean futures prices in China［D］. Lanzhou: Lanzhou University, 2014.)
［5］石波, 张冬青, 马开平，等. 改进RBF神经网络在我国大豆价格预测中的应用研究［J］. 大豆科学, 2016, 35(2): 310-314. (Shi B, Zhang D Q, Ma K P, et al. Soybean price prediction in China based on improved RBF neural network［J］. Soybean Science, 2016, 35(2): 310-314.)
［6］毛学峰, 贾伟. 大豆及制成品动态特征价格的实证研究［J］. 农业技术经济, 2016(7):73-80. (Mao X F, Jia W. An empirical study on the price of dynamic characteristics of soybean and manufactured goods［J］. Journal of Agrotechnical Economics, 2016(7):73-80.)
［7］Berwald D, Havenner A. Evaluating state space forecasts of soybean complex prices ［M］// Aoki M, et al .Applications of computer aided time series modeling. New York:Springer-Verlag, Inc. 1997.
［8］Arnade C, Hoffman L.The impact of price variability on cash/futures market relationships: Implications for market efficiency and price discovery［J］. Journal of Agricultural & Applied Economics, 2015, 47(4): 539-559.
［9］Adrangi B, Chatrath A, Raffiee K. Price discovery in the soybean futures market［J］. Journal of Business & Economics Research, 2011, 4(6): 77-88.
［10］Koenker R, Bassett G W. Regression quantiles［J］. Econometric, 1978, 46:33-50.
［11］Taylor J W. A quantile regression neural network approach to estimating the conditional density of multiperiod returns［J］. Journal of Forecasting, 2000, 19(4): 299-311.
［12］许启发, 蒋翠侠. 分位数局部调整模型及应用［J］. 数量经济技术经济研究, 2011(8): 115-133. (Xu Q F, Jiang C X. Quantile partial adjustment model and its application［J］. The Journal of Quantitative & Technical Economics, 2011(8): 115-133.)
［13］Cannon A J. Quantile regression neural networks: Implementation in R and application to precipitation downscaling［J］. Computers & Geosciences, 2011, 37: 1277-1284.
［14］何耀耀, 许启发, 杨善林等. 基于RBF神经网络分位数回归的电力负荷概率密度预测方法［J］. 中国电机工程学报, 2013, 33(1): 93-98. (He Y Y, Xu Q F, Yang S L, et al. A power load probability density forecasting method based on RBF neural network quantile regression［J］. Proceedings of the CSEE, 2013, 33(1): 93-98.)
［15］Mok T K, Liu H M, Ni Y X, et al. Tuning the fuzzy damping controller for UPFC through genetic algorithm with comparison to the gradient descent training［J］.Electrical Power and Energy Systems, 2005, 27: 275-283.
［16］刘欢, 张冬青. 基于分位数回归的国产大豆价格影响因素分析［J］. 大豆科学, 2014, 33(5): 759-763. (Liu H, Zhang D Q. Analysis on influencing factors of domestic soybean price based on quantile regression［J］. Soybean Science, 2014, 33(5): 759-763.)

Memo

Memo:
-
Last Update: 2017-03-15