RT Conference Proceedings
T1 Shuffle design to improve time series forecasting accuracy
A1 Peralta, Juan
A1 Gutiérrez Sánchez, Germán
A1 Sanchis de Miguel, María Araceli
AB 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.
PB IEEE
SN 978-1-4244-2958-5
YR 2009
FD 2009
LK http://hdl.handle.net/10016/9782
UL http://hdl.handle.net/10016/9782
LA eng
NO Proceeding of: IEEE Congress on Evolutionary Computation, CEC'09. May 18-21, 2009. Trondheim, Norway.
NO The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02-02.
DS e-Archivo
RD 28 abr. 2024