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Publisher: Springer

Issued date: 2010

Citation: Artificial neural networks : ICANN 2010, 20th International Conference, Proceedings, Part I. Springer, 2010 Lecture Notes in Computer Science, vol. 6352, pp. 50-53.

ISBN: 978-3-642-15818-6

ISSN: 0302-9743 Print1611-3349 Online

DOI: 10.1007-978-3-642-15819-3 7

Sponsor: The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02- 02.

Serie-No.: Lecture notes in computer science, vol. 6352

Publisher version:

Keywords: Evolutionary computation , Genetic algorithms , Artificial Neural Networks , Time Series , Forecasting , Ensembles

Rights: Springer-Verlag Berlin Heidelberg

Abstract: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 NeuraAccurate 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.+-

Description:Proceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010





Autor: Peralta, Juan; Gutiérrez, Germán; Sanchis, Araceli

Fuente: http://e-archivo.uc3m.es


Introducción



Universidad Carlos III de Madrid Repositorio institucional e-Archivo http:--e-archivo.uc3m.es Grupo de Control, Aprendizaje y Optimización de Sistemas (CAOS) DI - CAOS - Comunicaciones en Congresos y otros eventos 2010 Time series forecasting by evolving artificial neural networks using þÿ Shuffle , cross-validation and ensembles Peralta, Juan Springer Artificial neural networks : ICANN 2010, 20th International Conference, Proceedings, Part I. Springer, 2010 (Lecture Notes in Computer Science, vol.
6352), pp.
50-53. http:--hdl.handle.net-10016-9933 Descargado de e-Archivo, repositorio institucional de la Universidad Carlos III de Madrid Time Series Forecasting by Evolving Artificial Neural Networks Using “Shuffle”, Cross-Validation and Ensembles Juan Peralta, German Gutierrez, and Araceli Sanchis Computer Science Department, University Carlos III of Madrid Avenida de la Universidad 30 28911 Leganes, Spain {jperalta,ggutierr,masm}@inf.uc3m.es Abstract.
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. Keywords: Evolutionary Computation, Genetic Algorithms, Artificial Neural Networks, Time Series, Forecasting, Ensembles. 1 Introduction Time series forecasting is an essential research field due to its applications in several research, commercial and industry areas, and can be performed by Statistical metho...





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