Multivariate Time Series Prediction based on Multiple Kernel Extreme Learning Machine

被引：0
|
作者：
Wang, Xinying [1 ]
Han, Min [1 ]
机构：
[1] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian, Liaoning, Peoples R China
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a multiple kernel extreme learning machine (MKELM) is proposed for multivariate time series prediction. The multivariate time series is reconstructed in phase space, and a variable selection algorithm is then applied to form the compact and relevant input for the prediction model. On the basis of multiple kernel learning and extreme learning machine with kernels, multi different kernels is used in MKELM to present the dynamics of multivariate time series. A simulation example, prediction of Lorenz chaotic time series is conducted to demonstrate the effectiveness of the proposed method.
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收藏
页码：198 / 201
页数：4
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