Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

  • Mark van Heeswijk
  • Yoan Miche
  • Tiina Lindh-Knuutila
  • Peter A. J. Hilbers
  • Timo Honkela
  • Erkki Oja
  • Amaury Lendasse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5769)

Abstract

In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series. We verify that the method works on stationary time series and test the adaptivity of the ensemble model on a nonstationary time series. In the experiments, we show that the adaptive ensemble model achieves a test error comparable to the best methods, while keeping adaptivity. Moreover, it has low computational cost.

Keywords

time series prediction sliding window extreme learning machine ensemble models nonstationarity adaptivity 

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References

  1. 1.
    Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70(16-18), 2861–2869 (2007)CrossRefGoogle Scholar
  2. 2.
    Simon, G., Lendasse, A., Cottrell, M., Fort, J.-C., Verleysen, M.: Time series forecasting: Obtaining long term trends with self-organizing maps. Pattern Recognition Letters 26(12), 1795–1808 (2005)CrossRefGoogle Scholar
  3. 3.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006)CrossRefGoogle Scholar
  4. 4.
    Weigend, A., Gershenfeld, N.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading (1993)Google Scholar
  5. 5.
  6. 6.
    Breiman, L.: Bagging predictors. In: Machine Learning, pp. 123–140 (1996)Google Scholar
  7. 7.
    Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics 26, 322–330 (1998)MathSciNetMATHGoogle Scholar
  8. 8.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Secaucus (2006)MATHGoogle Scholar
  9. 9.
    Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)CrossRefGoogle Scholar
  10. 10.
    Raudys, S., Zliobaite, I.: The multi-agent system for prediction of financial time series. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 653–662. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L.I.: Classifier ensembles for changing environments. MCS, 1–15 (2004)Google Scholar
  12. 12.
    Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and Its Applications. John Wiley & Sons Inc., Chichester (1972)MATHGoogle Scholar
  13. 13.
    Myers, R.H.: Classical and Modern Regression with Applications, 2nd edn. Duxbury, Pacific Grove (1990)Google Scholar
  14. 14.
    Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)CrossRefMATHGoogle Scholar
  15. 15.
    Miche, Y., Sorjamaa, A., Lendasse, A.: OP-ELM: Theory, experiments and a toolbox. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 145–154. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mark van Heeswijk
    • 1
    • 3
  • Yoan Miche
    • 1
    • 2
  • Tiina Lindh-Knuutila
    • 1
    • 4
  • Peter A. J. Hilbers
    • 3
  • Timo Honkela
    • 1
  • Erkki Oja
    • 1
  • Amaury Lendasse
    • 1
  1. 1.Adaptive Informatics Research CentreHelsinki University of TechnologyTKKFinland
  2. 2.INPG Grenoble - Gipsa-Lab, UMR 5216GrenobleFrance
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands
  4. 4.International Computer Science Institute of University of CaliforniaBerkeleyUSA

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