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. © 2012 Springer-Verlag.
CITATION STYLE
Guo, Y., Li, X., Bai, G., & Ma, J. (2012). Time series prediction method based on LS-SVR with modified Gaussian RBF. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7664 LNCS, pp. 9–17). https://doi.org/10.1007/978-3-642-34481-7_2
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