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论文类型：会议论文

发表时间：2014-07-06

收录刊物：EI、CPCI-S、SCIE、Scopus

页面范围：198-201

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.