Moving average model for daily euro index in Europe with genetic algorithms and Comparing it with Box-Jenkins model
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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.