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Forecasting performance of ARMA models by Correction errors with Genetics Algorithms
Abstract
This paper presents a new method for determining the order and parameters of moving average in ARMA model, using a robustness method, is traditional gntics algorithms, by minimizing Akaike information criterion AIC and MSE. After we performing our models by iterative to reducing average relative error(used in forecasting phase) calling genetics algorithms, considers the output error and uses it as input again after reducing and normalizing the errors rate until error rate is very small by this method. Application of this method on airline data plane, the results show that the performance of iteration is better than the model before iteration and Box-Jenkins model.
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