Time series prediction method based on LS-SVR with modified Gaussian RBF

Yangming Guo, Xiaolei Li, Guanghan Bai, Jiezhong Ma

科研成果: 书/报告/会议事项章节会议稿件同行评审

22 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
9-17
页数9
版本PART 2
DOI
出版状态已出版 - 2012
活动19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, 卡塔尔
期限: 12 11月 201215 11月 2012

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编号PART 2
7664 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议19th International Conference on Neural Information Processing, ICONIP 2012
国家/地区卡塔尔
Doha
时期12/11/1215/11/12

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