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Indexed by:会议论文

Date of Publication:2014-07-06

Included Journals:EI、CPCI-S、SCIE、Scopus

Page Number:198-201

Abstract: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.