Time Series forecasting by evolving artificial neural networks using "Shuffle", cross-validation and ensembles

Authors:
Juan Peralta;German Gutierrez;Araceli Sanchis
Affiliations:
Computer Science Department, University Carlos III of Madrid, Leganes, Spain;Computer Science Department, University Carlos III of Madrid, Leganes, Spain;Computer Science Department, University Carlos III of Madrid, Leganes, Spain
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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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.