Chapter

Neural Information Processing

Volume 7664 of the series Lecture Notes in Computer Science pp 9-17

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

  • Yangming GuoAffiliated withSchool of Computer Science and Technology, Northwestern Polytechnical University
  • , Xiaolei LiAffiliated withSchool of Computer Science and Technology, Northwestern Polytechnical University
  • , Guanghan BaiAffiliated withDepartment of Mechanical Engineering, University of Alberta
  • , Jiezhong MaAffiliated withSchool of Computer Science and Technology, Northwestern Polytechnical University

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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