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Using Smoothing Splines in Time Series Prediction with Neural Networks

  • Uroš Lotrič
  • Andrej Dobnikar

Abstract

The smoothing spline based neural network is used for prediction of a trend from complex and noisy time series. First, the time series is smoothed by a cubic spline and then multilayered feedforward neural networks are applied to predict the parameters of the spline and by this the next values of the smoothed time series. The level of smoothing can be chosen by the smoothing parameter. We show that in the case of a complex time series like the bike tire sale, prediction of a trend with the smoothing spline based neural network gives us more reliable information than a classical prediction with the multilayered feedforward neural network.

Keywords

Neural Network Time Series Smoothing Parameter Thick Solid Line Thin Solid Line 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Guarnieri, S., Piazza, F., Uncini, A.: Multilayered Neural Networks with Adaptive Spline-based Activation Functions, Proceeding of the 1995 World Congress on Neural Networks, 1 (1995).Google Scholar
  2. [2]
    Harris, C. J., Moore, C. G., Brown, M.: Intelligent Control. Aspects of Fuzzy Logic and Neural Networks. World Scientific, 1994.Google Scholar
  3. [3]
    Masters, T.: Neural, Novel and Hybrid Algorithms for Time Series Prediction, John Wiley & Sons, 1995.Google Scholar
  4. [4]
    Reinsch, C. H.: Smoothing by Spline Functions. Numer. Math., 177–183, 10 (1967).MathSciNetMATHCrossRefGoogle Scholar
  5. [5]
    Craven, P., Wahba, G.: Smoothing Noisy Data with Spline Functions. Numer. Math., 377–403, 31 (1979).MathSciNetMATHCrossRefGoogle Scholar
  6. [6]
    Hutchinson, M. F., de Hoog, F. R.: Smoothing Noisy Data with Spline Functions. Numer. Math., 99–106, 47 (1985).MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    Golub, G. H., Heath, M., Wahba, G.: Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter. Technometrics, 215–223, 21 (1979).MathSciNetMATHCrossRefGoogle Scholar
  8. [8]
    Haykin, S.: Neural Networks, A Comprehensive Foundation. Macmillan College, New York, 1994.Google Scholar
  9. [9]
    Farin, G.: Curves and Surfaces for Computer Aided Geometric Design. Third Edition, Academic, San Diego, 1993.Google Scholar
  10. [10]
    Lotric, U.: Time Series Prediction with Smoothing Spline Based Neural Network. Masters Thesis, University of Ljubljana, Slovenia, 1997.Google Scholar
  11. [11]
    Lotric, U., Dobnikar, A.: Functional Neural Gas Network versus Smoothing Spline Based Neural Network. International ICSC Symposium, Engineering of Intelligent Systems, Tenerife, ICSC Academic Press, 132–135 (1998).Google Scholar

Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • Uroš Lotrič
    • 1
  • Andrej Dobnikar
    • 2
  1. 1.R&D Institute, Sava d.d.KranjSlovenia
  2. 2.Faculty of Computer and Information ScienceUniversity of LjubljanaSlovenia

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