Title :
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
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;
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
DOI :
10.1109/CSO.2011.33
