Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

被引：0

作者：

van Heeswijk, Mark ^{[1
]}

Miche, Yoan ^{[1
,2
]}

Lindh-Knuutila, Tiina ^{[1
,4
]}

Hilbers, Peter A. J. ^{[3
]}

Honkela, Timo ^{[1
]}

Oja, Erkki ^{[1
]}

Lendasse, Amaury ^{[1
]}

机构：

[1] Aalto Univ, Adapt Informat Res Ctr, POB 5400, Helsinki 02015, Finland

[2] INPG Grenoble Gipsa Lab, F-38402 Grenoble, France

[3] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands

[4] Int Comp Sci Inst Univ California, Berkeley, CA 94704 USA

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II | 2009年 / 5769卷

基金：

芬兰科学院;

关键词：

time series prediction; sliding window; extreme learning machine; ensemble models; nonstationarity; adaptivity;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series. We verify that the method works on stationary time series and test the adaptivity of the ensemble model on a nonstationary time series. In the experiments, we show that the adaptive ensemble model achieves a test error comparable to the best methods, while keeping adaptivity. Moreover, it has low computational cost.

引用

页码：305 / +

页数：2

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