Self-adaptive evolution strategies for ARMA model identification

This work presents the application of Evolutionary Computation techniques to the identification (order selection and parameter estimation) of an AutoRegressive Moving Average model (ARMA). Our method combines the effectiveness of the Multi Model Partitioning (MMP) theory with the robustness of the Genetic Algorithms (GAs) in order to give optimum estimations of the noise sequence embedded to the moving average terms of the model. Although the noise sequence´s coding is very complicated, the proposed algorithm succeeds better results compared to the classical methods, since it has the ability to search the whole values´ range. This is because, in contradiction with all the known classical methods, our algorithm is able to estimate with high precision the unknown parameters even in the case of large order in the moving average terms of the model.