Apply Grey Prediction in the Agriculture Production Price

Author

Jiajun Zong ; Quanyin Zhu

Author_Institution

Fac. of Comput. Eng., Huaiyin Inst. of Technol., Huaiyin, China

fYear

2012

fDate

2-4 Nov. 2012

Firstpage

396

Lastpage

399

Abstract

In order to get the excellent accuracy for price forecast in the agriculture products market, the Grey Prediction method is utilized to forecast the price of the agriculture products in this paper. Ten agriculture products, which extracted from Agricultural Bank of China at January, 2011 to December 2011, are selected to forecast the price about four weeks and compare the Mean Absolute Percentage Errors (MAPE) by Grey Method (GM) and RBF Neural Network (NN). Experiments demonstrate that the GM(1, 1) is not good for forecasting the agriculture products price and is not stable too. While the RBF NN is better then the GM(1, 1). Experiment results prove that this verdict is meaningful and useful to analyze and to research the price forecast in the agriculture products market.

Keywords

agricultural products; economic forecasting; grey systems; industrial economics; production engineering computing; radial basis function networks; Agricultural Bank of China; MAPE; RBF neural networks; RBFNN; agriculture production price; agriculture products market; grey prediction method; mean absolute percentage errors; price forecast; Accuracy; Artificial neural networks; Forecasting; Predictive models; Sugar; Grey prediction; MAPE; RBF NN; agriculture products; price forecast;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-3093-0

Type

conf

DOI

10.1109/MINES.2012.78

Filename

6405707