Multivariate time series prediction based on multiple kernel extreme learning machine
Xinying Wang, Min Han · 2014
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.