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论文类型：期刊论文

发表时间：2014-12-05

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、ESI高被引论文、Scopus

卷号：145

页面范围：90-97

ISSN号：0925-2312

关键字：Online; Time series; Extreme learning machine; Support vector machine; Nonstationary

摘要：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.