A Hybrid Model of Rough Sets and Shannon Entropy for Building a Foreign Trade Forecasting System

Author

Gong, Ke ; Liu, Mingwu ; Fang, Yong ; Zhang, Xia

Author_Institution

Sch. of Manage., Chongqing Jiaotong Univ., Chongqing, China

fYear

2011

fDate

15-19 April 2011

Firstpage

7

Lastpage

11

Abstract

Forecasting the volume of foreign trade is important to policy formulation for local governments. This study proposes a machine-learning algorithm as a forecasting tool that is based on Rough sets and Shannon entropy. This study uses historical data from a large municipal to examine the proposed forecasting tool. The results suggest that this tool can be useful in specific trade decisions with unique characteristics and requirements.

Keywords

forecasting theory; information theory; international trade; rough set theory; Shannon entropy; foreign trade forecasting system; hybrid model; machine learning algorithm; policy formulation; rough sets; unique characteristics; unique requirements; Data models; Entropy; Forecasting; Information systems; Predictive models; Rough sets; Time series analysis; Expert system; Shannon entropy; Time series forecasting; combining forecast; model selection; rough sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on

Conference_Location

Yunnan

Print_ISBN

978-1-4244-9712-6

Electronic_ISBN

978-0-7695-4335-2

Type

conf

DOI

10.1109/CSO.2011.33

Filename

5957599