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Mathematical Problems in Engineering
Volume 2014, Article ID 135862, 10 pages
http://dx.doi.org/10.1155/2014/135862
Research Article

A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting

1College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
2College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China

Received 2 February 2014; Accepted 20 May 2014; Published 23 June 2014

Academic Editor: Wei-Chiang Hong

Copyright © 2014 Youzhu Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P) filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.