PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Long-term prediction of time series using a parsimonious set of inputs and LS-SVM
Jarkko Tikka and Jaakko Hollmen
In: European Symposium on Time Series Prediction (ESTSP 2007), 7 Feb - 9 Feb 2007, Espoo, Finland.

Abstract

Time series prediction is an important problem in many areas of science and engineering. We investigate the use of a parsimonious set of autoregressive variables in the long-term prediction task using the direct prediction approach. We use a fast input selection algorithm on a large set of autoregressive variables for different direct predictors, and train non- linear models (LS-SVM and a committee of LS-SVM) on the parsimonious set of non-contiguous set of autoregressive variables. Results will be shown for the time series competition task.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3614
Deposited By:Jaakko Hollmen
Deposited On:13 February 2008