GDP Growth Rate Prediction Based on BP Neural Network and Support Vector Machine

被引：0

作者：

Zhou Shun ^{[1
]}

Yue Xiaoguang ^{[2
]}

机构：

[1] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Peoples R China

[2] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Peoples R China

来源：

PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON INNOVATION AND MANAGEMENT, VOLS I AND II | 2014年

关键词：

GDP; Growth rate; BP neural network; Support vector machine;

D O I：

暂无

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

In order to predict GDP growth rate in China, BP Neural Network (BPNN) and Support Vector Machine (SVM) are used for GDP growth rate prediction. BP Neural Network and Support Vector Machine prediction models are established by Matlab. BPNN and SVM are both effective methods for GDP growth rate prediction. The performance of SVM is better than BPNN. The average absolute error of SVM is 14.11. The next research will focus on the improving for SVM method.

引用

页码：1263 / 1266

页数：4

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