Long-term prediction of time series using NNE-based projection and OP-ELM

Antti Sorjamaa, Yoan Miche, Robert Weiss, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

20 Citations (Scopus)

Abstract

This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then, after the network is first created using the original ELM, the selection of the most relevant nodes is performed by using a Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances, leading to an Optimally-Pruned ELM (OP-ELM). Finally, the prediction accuracy of the global methodology is demonstrated using the ESTSP 2008 Competition and Poland Electricity Load datasets.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages2674-2680
Number of pages7
ISBN (Electronic)978-1-4244-1821-3
DOIs
Publication statusPublished - 2008
MoE publication typeA4 Article in a conference publication
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 1 Jun 20088 Jun 2008

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
Country/TerritoryChina
CityHong Kong
Period1/06/088/06/08

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