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En este trabajo se utilizan Algoritmos Geneticos Autoadaptables para desarrollar nuevos modelos de Series de Tiempo que son equivalentes a los modelos clasicos ARIMA. La ventaja de estos nuevos modelos es que el numero de terminos se puede elegir de antemano consiguiendo con esto que, conforme el numero de terminos se incrementa, tambien lo haga la precision del modelo. Con los modelos desarrollados en este trabajo se participo en la competencia NN3 Neural Forescasting Competition y se obtuvo el tercer lugar al construir modelos para todos los problemas con solo cuatro terminos."
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