Shuffle design to improve time series forecasting accuracy

In this work new improvements from a previous approach of an automatic design of artificial neural networks applied to forecast time series is tackled. The automatic process to design artificial neural networks is carried out by a genetic algorithm. These improvements, in order to get an accurate forecasting, are related with: to shuffle train and test patterns obtained from time series values and improving the fitness function during the global learning process (i.e. genetic algorithm) using a new patterns set called validation apart of the two used till the moment (i.e. train and test). The object of this study is to try to improve the final forecasting getting an accurate system. Results of the artificial neural networks got by our system to forecast a set of famous time series are shown.