PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Time Series Prediction with Variational Bayesian Nonlinear State-Space Models
Matti Tornio, Antti Honkela and Juha Karhunen
In: European Symposium on Time Series Prediction (ESTSP 2007), 7-9 Feb 2007, Espoo, Finland.

Abstract

In this paper the variational Bayesian method for learning nonlinear state-space models introduced by Valpola and Karhunen in 2002 is applied to prediction in the ESTSP'07 time series prediction competition data set. The data set is pre-processed by approximately removing the periodic component of the data and the nonlinear state-space model is only learned on the residuals. The model uses multilayer perceptron (MLP) networks to model the nonlinearities of the system which allows the modelling of complex dynamical processes. The variational Bayesian learning approach is resistant to overfitting and allows comparison of different model structures using the derived lower bound on marginal log-likelihood. The desired predictions are evaluated as the mean of a Monte Carlo approximation of the predictive distribution.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3233
Deposited By:Antti Honkela
Deposited On:27 January 2008