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Proceedings of the 2nd International Symposium on Information Processing (ISIP 2009)

Huangshan, China, August 21-23, 2009

Editors: Fei Yu, Jian Shu, and Guangxue Yue

AP Catalog Number: AP-PROC-CS-09CN002

ISBN: 978-952-5726-02-2 (Print), 978-952-5726-03-9 (CD-ROM)

Page(s): 171-174

GDP Forecasting Based on Online Weighted Least Squares Support Vector Machine

Xuan Du

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In order to improve the prediction accuracy of chaotic time series, a chaotic time series forecasting method based on online weighted least squares support vector machine regression (WLS-SVM) is proposed. In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system. The model of the system is refreshed on-line with the rolling of the time window. In order to make full use of data information, different weights are assigned to different data in the sliding time window. Online WLS-SVM prediction method is applied to predict the GDP data in macro economy. Results show that the proposed method can be realized easily and has good performance in robustness and precision in chaotic time series prediction.

Index Terms

online weighted least squares support vector machine, regression, chaotic time series, forecasting, sliding time window, GDP

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