RT Conference Proceedings
T1 Time series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles
A1 Peralta, Juan
A1 Gutiérrez Sánchez, Germán
A1 Sanchis de Miguel, María Araceli
AB Accurate time series forecasting are important for several business, research, and application of engineering systems. Evolutionary Neural Networks are particularly appealing because of their ability to design, in an automatic way, a model (an Artificial Neural Network) for an unspecified nonlinear relationship for time series values. This paper evaluates two methods to obtain the pattern sets that will be used by the artificial neural network in the evolutionary process, one called ”shuffle” and another one carried out with cross-validation and ensembles. A study using these two methods will be shown with the aim to evaluate the effect of both methods in the accurateness of the final forecasting.
PB Springer
SN 978-3-642-15818-6
SN 0302-9743 (Print)
SN 1611-3349 (Online)
YR 2010
FD 2010
LK https://hdl.handle.net/10016/9933
UL https://hdl.handle.net/10016/9933
LA eng
NO Proceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010
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 19 may. 2024