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

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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.

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Acknowledgments

The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02-02. The authors want to thank specially Ramon Sagarna for introducing the subject of EDA.

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Authors and Affiliations

  1. Computer Science Department, Group CAOS, University Carlos III of Madrid, Avenida de la Universidad 30, 28911, Leganes, Spain

    Juan Peralta Donate, Germán Gutiérrez Sánchez & Araceli Sanchis de Miguel

  2. School of Computer Science and Information Technology, Royal Melbourne Institute of Techonology, Melbourne, VIC, Australia

    Xiaodong Li

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  1. Juan Peralta Donate
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  2. Xiaodong Li
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  3. Germán Gutiérrez Sánchez
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  4. Araceli Sanchis de Miguel
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Correspondence to Juan Peralta Donate.

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Donate, J.P., Li, X., Sánchez, G.G. et al. Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput & Applic 22, 11–20 (2013). https://doi.org/10.1007/s00521-011-0741-0

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