Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-01

AUTHORS

Juan Peralta Donate, Xiaodong Li, Germán Gutiérrez Sánchez, Araceli Sanchis de Miguel

ABSTRACT

Time series forecasting is an important tool to support both individual and organizational decisions (e.g. planning production resources). In recent years, a large literature has evolved on the use of evolutionary artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. In this work, two new approaches of a previous system, automatic design of artificial neural networks (ADANN) applied to forecast time series, are tackled. In ADANN, the automatic process to design artificial neural networks was carried out by a genetic algorithm (GA). This paper evaluates three methods to evolve neural networks architectures, one carried out with genetic algorithm, a second one carried out with differential evolution algorithm (DE) and the last one using estimation of distribution algorithms (EDA). A comparative study among these three methods with a set of referenced time series will be shown. In this paper, we also compare ADANN forecasting ability against a forecasting tool called Forecast Pro® (FP) software, using five benchmark time series. The object of this study is to try to improve the final forecasting getting an accurate system. More... »

PAGES

11-20

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-011-0741-0

DOI

http://dx.doi.org/10.1007/s00521-011-0741-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032539884


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