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Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification

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2 Author(s)
Mao XueMin ; Manage. Sch., Hefei Univ. of Technol., Hefei ; Yang Jie

In this paper, an introduction of traditional time series prediction model using SVM has been first given, and then followed by description of a new network training algorithm and a nonlinear regression algorithm of support vector machine which are based on classification. Compared with traditional SVM regression algorithm, CSVR algorithm is less sensitive and more robust. It is another advantage that the value of the parameters can be set according to individual situation. More importantly, this method can also escape from over-fitting. Finally, an analysis of this new method has been given to demonstrate the validity of this method.

Published in:

Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Date of Conference:

Nov. 28 2006-Dec. 1 2006