Publication: Time series forecasting by evolving artificial neural networks using “Shuffle”, cross-validation and ensembles
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ISSN: 0302-9743 (Print)
ISSN: 1611-3349 (Online)
ISBN: 978-3-642-15818-6
Publication date
2010
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Springer
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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.
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Proceeding of: ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010
Keywords
Evolutionary computation, Genetic algorithms, Artificial Neural Networks, Time Series, Forecasting, Ensembles
Bibliographic citation
Artificial neural networks : ICANN 2010, 20th International Conference, Proceedings, Part I. Springer, 2010 (Lecture Notes in Computer Science, vol. 6352), pp. 50-53.