Advertisement

Time Series Prediction Using New Adaptive Kernel Estimators

  • Marcin Michalak
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

This short article describes two kernel algorithms of the regression function estimation. First of them is called HASKE and has its own heuristic of the h parameter evaluation. The second is a hybrid algorithm that connects SVM and the HASKE in such way that the definition of local neighborhood bases on the definition of the h–neighborhood from HASKE. Both of them are used as predictors for time series.

Keywords

Support Vector Machine Support Vector Regression Multivariate Adaptive Regression Spline Kernel Estimator Time Series Prediction 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    WIG20 historical data, http://stooq.pl/q/d/?s=wig20
  2. 2.
    Gasser, T., Kneip, A., Kohler, W.: A Flexible and Fast Method for Automatic Smoothing. Annals of Statistics 86, 643–652 (1991)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Terrell, G.R.: The Maximal Smoothing Principle in Density Estimation. Annals of Statistics 85, 470–477 (1990)MathSciNetGoogle Scholar
  4. 4.
    Fan, J., Gijbels, I.: Variable Bandwidth and Local Linear Regression Smoothers. Annals of Statistics 20, 2008–2036 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Terrell, G.R., Scott, D.W.: Variable Kernel Density Estimation. Annals of Statistics 20, 1236–1265 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Turlach, B.A.: Bandwidth Selection in Kernel Density Estimation: A Review. Universite Catholique de Louvain, Technical report (1993)Google Scholar
  7. 7.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proc. of the 5th annual workshop on Computational Learning Theory, Pittsburgh, pp. 144–152 (1992)Google Scholar
  8. 8.
    Fernandez, R.: Predicting Time Series with a Local Support Vector Regression Machine. In: Proc. of the ECCAI Advanced Course on Artificial Intelligence (1999)Google Scholar
  9. 9.
    Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Cao, L.J., Tay, F.E.H.: Svm with adaptive parameters in financial time series forecasting. IEEE Trans. on Neural Networks 14, 1506–1518 (2003)CrossRefGoogle Scholar
  11. 11.
    Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10, 215–236 (1996)CrossRefGoogle Scholar
  12. 12.
    Friedman, J.H.: Multivariate Adaptive Regression Splines. Annals of Statistics 19, 1–141 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, Boca Raton (1990)zbMATHGoogle Scholar
  14. 14.
    Michalak, M., Sta̧por, K.: Estymacja ja̧drowa w predykcji szeregów czasowych. Studia Informatica 29 3A (78), 71–90 (2008)Google Scholar
  15. 15.
    Michalak, M.: Możliwości poprawy jakości usług w transporcie miejskim poprzez monitoring natȩżenia potoków pasażerskich. ITS dla Śla̧ska, Katowice (2008)Google Scholar
  16. 16.
    Sikora, M., Kozielski, M., Michalak, M.: Innowacyjne narzȩdzia informatyczne analizy danych. Wydział Transportu, Gliwice (2008)Google Scholar
  17. 17.
    de Boor, C.: A practical guide to splines. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  18. 18.
    Gajek, L., Kałuszka, M.: Wnioskowanie statystyczne, WNT, Warszawa (2000)Google Scholar
  19. 19.
    Koronacki, J., Ćwik, J.: Statystyczne systemy ucza̧ce siȩ. WNT, Warszawa (2005)Google Scholar
  20. 20.
    Kulczycki, P.: Estymatory ja̧drowe w analizie systemowej. WNT, Warszawa (2005)Google Scholar
  21. 21.
    Box, G.E.P., Jenkins, G.M.: Analiza szeregów czasowych. PWN, Warszawa (1983)Google Scholar
  22. 22.
    Gasser, T., Muller, H.G.: Estimating Regression Function and Their Derivatives by the Kernel Method. Scandinavian Journal of Statistics 11, 171–185 (1984)zbMATHMathSciNetGoogle Scholar
  23. 23.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)CrossRefzbMATHGoogle Scholar
  24. 24.
    Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probability and Its Applications 14, 153–158 (1969)CrossRefGoogle Scholar
  25. 25.
    Nadaraya, E.A.: On estimating regression. Theory of Probability and Its Applications 9, 141–142 (1964)CrossRefGoogle Scholar
  26. 26.
    Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  27. 27.
    Taylor, J.S., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  28. 28.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1988)Google Scholar
  29. 29.
    Watson, G.S.: Smooth Regression Analysis. Sankhya - The Indian Journal of Statistics 26, 359–372 (1964)zbMATHGoogle Scholar
  30. 30.
    Cleveland, W.S., Devlin, S.J.: Locally Weighted Regression. Jour. of the Am. Stat. Ass. 83, 596–610 (1988)CrossRefzbMATHGoogle Scholar
  31. 31.
    Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, Boca Raton (1995)CrossRefzbMATHGoogle Scholar
  32. 32.
    Smola, A.J.: Regression Estimation with Support Vector Learning Machines. Technische Universität München (1996)Google Scholar
  33. 33.
    Muller, K.R., Smola, A.J., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting Time Series with Support Vector Machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)Google Scholar
  34. 34.
    Huang, K., Yang, H., King, I., Lyu, M.: Local svr for Financial Time Series Prediction. In: Proc. of IJCNN 2006, pp. 1622–1627 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marcin Michalak
    • 1
  1. 1.Silesian Insitute of TechnologyInstitute of Computer ScienceGliwicePoland

Personalised recommendations