Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification

  • Authors:

  • MAO XueMin;YANG Jie

  • Affiliations:

  • Hefei University of Technology;Hefei University of Technology

  • Venue:
  • CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
  • Year:
  • 2006

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Abstract

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.