Evolutionary multi-model estimators for ARMA system modeling and time series prediction

Abstract

In this work, a well-tested evolutionary method for system modeling and time series prediction is presented. The method combines the effective-ness of adaptive multi model partitioning filters and GAs' robustness. Specifically, the a posteriori probability that a specific model, of a bank of the conditional models, is the true model can be used as fitness function for the GA. In this way, the algorithm identifies the true model even in the case where it is not included in the filters' bank and is able to accurately forecast the short-term evolution of the system. The method is not restricted to the Gaussian case; it is computationally efficient and is applicable to on-line/adaptive system modeling and time series prediction. © Springer-Verlag Berlin Heidelberg 2003.