TY - GEN
T1 - Time series prediction method based on LS-SVR with modified Gaussian RBF
AU - Guo, Yangming
AU - Li, Xiaolei
AU - Bai, Guanghan
AU - Ma, Jiezhong
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Gaussian RBF
KW - Least squares support vector regression (LS-SVR)
KW - Time series prediction
UR - http://www.scopus.com/inward/record.url?scp=84869011767&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34481-7_2
DO - 10.1007/978-3-642-34481-7_2
M3 - 会议稿件
AN - SCOPUS:84869011767
SN - 9783642344800
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 9
EP - 17
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
T2 - 19th International Conference on Neural Information Processing, ICONIP 2012
Y2 - 12 November 2012 through 15 November 2012
ER -