A neural network model based on the multi-stage optimization approach for short-term food price forecasting in China

Authors:
Zou Haofei;Xia Guoping;Yang Fangting;Yang Han
Affiliations:
School of Economics and Management, Beihang University, No. 37, Xue Yuan Road, HaiDian District, Beijing 100083, China;School of Economics and Management, Beihang University, No. 37, Xue Yuan Road, HaiDian District, Beijing 100083, China;Beijing Simulation Center, Beijing 100854, China;Production Technical Institute of the General Logistics Department, Beijing 100010, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

Citing 5
Cited 5

The use of ARIMA models for reliability forecasting and analysis

Proceedings of the 23rd international conference on on Computers and industrial engineering
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction

Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Computers and Operations Research
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

Journal of Management Information Systems

Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study

Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network

Expert Systems with Applications: An International Journal
A genetic network programming with learning approach for enhanced stock trading model

Expert Systems with Applications: An International Journal
A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Expert Systems with Applications: An International Journal
Application of Levy processes and Esscher transformed martingale measures for option pricing in fuzzy framework

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	12.06

Visualization

Abstract

Many studies have demonstrated that back-propagation neural network can be effectively used to uncover the nonlinearity in the financial markets. Unfortunately, back-propagation algorithm suffers the problems of slow convergence, inefficiency, and lack of robustness. This paper introduces a multi-stage optimization approach (MSOA) used in back-propagation algorithm for training neural network to forecast the Chinese food grain price. We divide the training sample of neural network into two parts considering the truth that the recent observations is more important than the older ones. Firstly, we use the first training sample to train the neural network and achieve the network structure. Secondly, we continue to use the second training sample to further optimize the structure of neural network based on the previous step. Empirical results show that MSOA overcomes the weakness of conventional BP algorithm to some extend. Furthermore the neural network based on MSOA can improve the forecasting performance significantly in terms of the error and directional evaluation measurements. The paper also proves accurate price estimation may not be a good predictor of the direction of change in the price levels in food market. The neural network based on MSOA can be used as an alternative forecasting method for future Chinese food price forecasting.