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Selected an Stacking ELMs for Time Series Prediction

Authors:

Qun DaiAuthors Info & Claims

Neural Processing Letters, Volume 44, Issue 3

Pages 831 - 856

https://doi.org/10.1007/s11063-016-9499-9

Published: 01 December 2016 Publication History

Abstract

Extreme learning machine (ELM) has several interesting and significant features. In this paper, a novel pruned Stacking ELMs (PS-ELMs) algorithm for time series prediction (TSP) is proposed. It employs ELM as the level-0 algorithm to train several models for Stacking. And our previously proposed reduce-error pruning for TSP (ReTSP)-Trend pruning technique is used to solve the problem that the level-0 learners might make many correlated error predictions. ReTSP-Trend refers to an evaluation measure for reduce-error pruning for TSP (ReTSP), which takes into account the time series trend and the forecasting error direction. What's more, ELM and simple averaging are used to generate the level-1 model. With the development of PS-ELMs, firstly, those essential advantages of ELM will be naturally inherited. Secondly, those specific defects of ELM are ameliorated to some extent, with the help of ensemble pruning paradigm. Thirdly, ensemble pruning is employed to raise the robustness and accuracy of time series forecasting, making up for the shortages of the existing research. Fourthly, our previously proposed pruning measure ReTSP-Trend is employed in PS-ELMs, which indeed guarantees that the remaining predictor which supplements the subensemble the most will be selected. And finally, the development of PS-ELMs will promote our investigation to the popular ensemble technique of Stacked Generalization. The experimental results on four benchmark financial time series datasets verified the validity of the proposed PS-ELMs algorithm.

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https://dl.acm.org/doi/10.1007/s11063-023-11248-7
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Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 44, Issue 3

December 2016

309 pages

ISSN:1370-4621

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Copyright © Copyright © 2016 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2016

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Cited By

Sakib MMustajab SAlam M(2025)Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directionsCluster Computing10.1007/s10586-024-04684-028:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10586-024-04684-0
Tsalikidis NMystakidis ATjortjis CKoukaras PIoannidis D(2024)Energy load forecasting: one-step ahead hybrid model utilizing ensemblingComputing10.1007/s00607-023-01217-2106:1(241-273)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00607-023-01217-2
Zhang YDai YWu Q(2023)A Novel Regularization Paradigm for the Extreme Learning MachineNeural Processing Letters10.1007/s11063-023-11248-755:6(7009-7033)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11063-023-11248-7
Liang WLi XHe XLiu XZhang X(2018)Supervised ranking framework for relationship prediction in heterogeneous information networksApplied Intelligence10.1007/s10489-017-1044-748:5(1111-1127)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10489-017-1044-7
Ahmed TSrivastava A(2017)An automated approach to estimate human interestApplied Intelligence10.1007/s10489-017-0947-747:4(1186-1207)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10489-017-0947-7

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