Chaos forecasting model for GDP based on neural networks error-correction

被引：0

作者：

Ao Shan ^{[1
]}

Tang Shoulian ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Econ & Management Sch, Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING, FINANCE ANALYSIS SECTION | 2007年

关键词：

non-linear dynamics; neural networks; chaos; logistic map; GDP;

D O I：

暂无

中图分类号：

K9 [地理];

学科分类号：

0705 ;

摘要：

To perform a simulation forecast about the increasing rate for 2004-2006;annual GDP based on chaotic attractors, aiming at the shortcoming that chaotic time series can not fit the actual fluctuation of small sample discrete data very well, especially for long-term economic forecast errors. This paper makes use of BP neural network to predict the fitting errors above, and to correct the final results based on the prediction. The three-year average relative error rate is up to 0.553%, the prediction accuracy has been improved significantly.

引用

页码：1237 / 1243

页数：7

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