期刊
NEUROCOMPUTING
卷 145, 期 -, 页码 90-97出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2014.05.068
关键词
Online; Time series; Extreme learning machine; Support vector machine; Nonstationary
资金
- National Natural Science Foundation of China [61374154, 61074096]
- National Basic Research Program of China [2013CB430403]
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.
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