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IJCNN
2008
IEEE

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

10 years 5 months ago
Long-term prediction of time series using NNE-based projection and OP-ELM
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.
Antti Sorjamaa, Yoan Miche, Robert Weiss, Amaury L
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Antti Sorjamaa, Yoan Miche, Robert Weiss, Amaury Lendasse
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