Time Series Prediction Method Based on LS-SVR with Modified Gaussian RBF

  • Yangming Guo
  • Xiaolei Li
  • Guanghan Bai
  • Jiezhong Ma
Conference paper

DOI: 10.1007/978-3-642-34481-7_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)
Cite this paper as:
Guo Y., Li X., Bai G., Ma J. (2012) Time Series Prediction Method Based on LS-SVR with Modified Gaussian RBF. In: Huang T., Zeng Z., Li C., Leung C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg

Abstract

LS-SVR is widely used in time series prediction. For LS-SVR, the selection of appropriate kernel function is a key issue, which has a great impact with the prediction accuracy. Compared with some other feasible kernel functions, Gaussian RBF is always selected as kernel function due to its good features. As a distance functions-based kernel function, Gaussian RBF also has some drawbacks. In this paper, we modified the standard Gaussian RBF to satisfy the two requirements of distance functions-based kernel functions which are fast damping at the place adjacent to the test point and keeping a moderate damping at infinity. The simulation results indicate preliminarily that the modified Gaussian RBF has better performance and can improve the prediction accuracy with LS-SVR.

Keywords

Least squares support vector regression (LS-SVR) Gaussian RBF Time series prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yangming Guo
    • 1
  • Xiaolei Li
    • 1
  • Guanghan Bai
    • 2
  • Jiezhong Ma
    • 1
  1. 1.School of Computer Science and TechnologyNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Mechanical EngineeringUniversity of AlbertaEdmontonCanada

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