Online sequential prediction for nonstationary time series with new weight-setting strategy using extreme learning machine
type: Item
instance of: scholarly article
title: Online sequential prediction for nonstationary time series with new weight-setting strategy using extreme learning machine
zbMATH Open document ID: 1395.62276
DOI: 10.1155/2015/484093
author: Wentao Mao
author: Jinwan Wang
author: Liyun Wang
author: Mei Tian
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Accurate and fast prediction of nonstationary time series is challenging and of great interest in both practical and academic areas. In this paper, an online sequential extreme learning machine with new weighted strategy is proposed for nonstationary time series prediction. First, a new leave-one-out (LOO) cross-validation error estimation for online sequential data is proposed based on inversion of block matrix. Second, a new weighted strategy based on the proposed LOO error estimation is proposed. This strategy ranks the samples' importance by means of the LOO error of each new added sample and then assigns various weights. Performance comparisons of the proposed method with other existing algorithms are presented based on chaotic and real-world nonstationary time series data. The results show that the proposed method outperforms the classical ELM and OS-ELM in terms of generalization performance and numerical stability.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6926455
Wikidata QID: Q59118704
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/484093
OpenAlex ID: W1514926545
cites work: Q3658782
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description: scientific article; zbMATH DE number 6926455