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Generalized Regression Neural Network for Forecasting Time Series with Multiple Seasonal Cycles

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Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

This paper presents a method of forecasting time series with multiple seasonal cycles based on Generalized Regression Neural Network. This is a memory-based, fast learned and easy tuned type of neural network. The time series is preprocessed to define input and output patterns of seasonal cycles, which simplifies the forecasting problem. The method is useful for forecasting nonstationary time series with multiple seasonal cycles and trend. The model learns with the help of differential evolution or simple enumerative method. The performance of the proposed method is compared with that of other forecasting methods based on Nadaraya-Watson estimator, neural networks, ARIMA and exponential smoothing. Application examples confirm valuable properties of the proposed method and its highest accuracy among the methods considered.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200, Czestochowa, Poland

    Grzegorz Dudek

Authors
  1. Grzegorz Dudek
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Corresponding author

Correspondence to Grzegorz Dudek .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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© 2015 Springer International Publishing Switzerland

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Dudek, G. (2015). Generalized Regression Neural Network for Forecasting Time Series with Multiple Seasonal Cycles. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_73

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_73

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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