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Indexed by:期刊论文

Date of Publication:2014-12-05

Journal:NEUROCOMPUTING

Included Journals:SCIE、ESI高被引论文、Scopus

Volume:145

Page Number:90-97

ISSN No.:0925-2312

Key Words:Online; Time series; Extreme learning machine; Support vector machine; Nonstationary

Abstract:In this paper, an online sequential extreme learning machine with kernels (OS-ELMK) has been proposed for nonstationary time series prediction. An online sequential learning algorithm, which can learn samples one-by-one or chunk-by-chunk, is developed for extreme learning machine with kernels. A limited memory prediction strategy based on the proposed OS-ELMK is designed to model the nonstationary time series. Performance comparisons of OS-ELMK with other existing algorithms are presented using artificial and real life nonstationary time series data. The results show that the proposed OS-ELMK produces similar or better accuracies with at least an order-of-magnitude reduction in the learning time. (C) 2014 Elsevier B.V. All rights reserved.