Peralta, JuanGutiérrez Sánchez, GermánSanchis de Miguel, María Araceli2010-12-162010-12-162009IEEE Congress on Evolutionary Computation (CEC 2009), IEEE, 2009, p.741-748978-1-4244-2958-5http://hdl.handle.net/10016/9782Proceeding of: IEEE Congress on Evolutionary Computation, CEC'09. May 18-21, 2009. Trondheim, Norway.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.application/pdftext/plaineng© IEEEArtificial neural networksAutomatic designGenetic algorithmsShuffle designTime series forecasting accuracyconference paperInformática10.1109/CEC.2009.4983019open access741748IEEE Congress on Evolutionary Computation (CEC 2009)