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Moving average model for daily euro index in Europe with genetic algorithms and Comparing it with Box-Jenkins model

R. Rahal

Abstract



This paper presents a comparison between moving average model with genetic algorithms method and Box-Jenkins model for daily euro index of Europe at 09/01/2017. Taking the Akaike information criterion AIC as the objective function to optimize it with binary genetic algorithms, and resolve the problem of unknown errors by centring the difference between observations and his mean, introduce a good model for the forecast. And comparing this model with Box-Jenkins model show us that the model of genetic algorithms is better than the Box-Jenkins model and this method can be used to improve the forecasting of the random problem.

Keywords


genetics algorithms; moving average; Box-Jenkins; daily euro index.

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