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Indexed by:会议论文

Date of Publication:2013-10-13

Included Journals:EI、CPCI-S、Scopus

Page Number:983-987

Abstract:Chaotic time series prediction has received considerable attention in the last few years. Although many studies have been conducted in the field, there is little attention focused on multivariate time series prediction. Considering this problem, a multi reservoir support vector echo state machine(MRSVESM) based on multi kernel learning and echo state networks is proposed in this paper. The single reservoir approach may be ineffective on multivariate time series prediction, as it is not able to character multi time scale dynamics. The MRSVESM use multi different time scale reservoirs to present the dynamics of multivariate time series and replaced the "kernel trick" with "reservoir trick", that is, performed multi kernel learning in the high dimension "reservoir"state space. Two simulation examples, prediction of Lorenz chaotic time series and prediction of sunspots and the Yellow River annual runoff time series are conducted to demonstrate the effectiveness of the proposed method.