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

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 artiicial time series, generated from the Mackey-Glass equation, and time series from a real world system consisting of physiological data of apnea patients.