Title: A Bayesian nonlinear support vector machine error correction model
Authors: Van Gestel, T ×
Espinoza, M
Baesens, Bart
Suykens, Johan
Brasseur, C
De Moor, Bart #
Issue Date: Mar-2006
Publisher: Wiley
Series Title: Journal of Forecasting vol:25 issue:2 pages:77-100
Abstract: The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel-based implicit nonlinear mapping to a high-dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade-offs between model complexity and in-sample model accuracy. From straightforward primal--dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel-based prediction is successfully applied to out-of-sample prediction of an aggregated equity price index for the European chemical sector. Copyright (c) 2006 John Wiley & Sons, Ltd.
ISSN: 0277-6693
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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