NDLI: Selected an Stacking ELMs for Time Series Prediction

Content Provider	Springer Nature Link
Author	Ma, Zhongchen Dai, Qun
Copyright Year	2016
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.
Starting Page	831
Ending Page	856
Page Count	26
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	44
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2016-01-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Extreme learning machine (ELM) Stacked Generalization (Stacking) Reduce-error pruning for time series prediction (ReTSP) ReTSP-Trend Financial time series forecasting Pruned Stacking extreme learning machines (PS-ELMs) Artificial Intelligence (incl. Robotics) Complex Systems Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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