Structure optimization of density estimation models applied to regression problems with dynamic noise

Authors:

Claus Weihs	Werner von Seelen
Fachbereich Statistik	Institut für Neuroinformatik
Universität Dortmund	Ruhr-Universität Bochum
44221 Dortmund, Germany	44780 Bochum, Germany

Abstract:

In this paper we deal with the problem of model selection for time series forecasting with dynamical noise and missing data. We employ an evolutionary algorithm to the optimization of a mixture of densities model in order to estimate, via a log-likelihood based quality measure, the joint probability density of the data. We apply our method to the prediction of both artificial time series, generated from the Mackey-Glass equation, and time series from a real world system consisting of physiological data of apnea patients.

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Other information:

• Related work from the authors:
  Martin Kreutz, Anja M. Reimetz, Bernhard Sendhoff, Claus Weihs and Werner von Seelen. Optimisation of Density Estimation Models with Evolutionary Algorithms. In A.E. Eiben, Th. Bäck, M. Schoenauer and H.P. Schwefel, editors, Parallel Problem Solving from Nature - PPSN V, pages 998-1007, Lecture Notes in Computer Science 1498, Springer, 1998.
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