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Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data...
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,...
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