PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Long-Term Prediction of Time Series using NNE-based Projection and OP-ELM
Antti Sorjamaa, Yoan Miche, Robert Weiss and Amaury Lendasse
In: IEEE World Conference on Computational Intelligence, June 2008, Hong Kong.


This paper proposes a combination of methodolo- gies 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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4796
Deposited By:Amaury Lendasse
Deposited On:24 March 2009