Nothing Special   »   [go: up one dir, main page]

Skip to main content

Time Series Prediction Using New Adaptive Kernel Estimators

  • Chapter
Computer Recognition Systems 3

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

  • 1050 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. WIG20 historical data, http://stooq.pl/q/d/?s=wig20

  2. Gasser, T., Kneip, A., Kohler, W.: A Flexible and Fast Method for Automatic Smoothing. Annals of Statistics 86, 643–652 (1991)

    MATH  MathSciNet  Google Scholar 

  3. Terrell, G.R.: The Maximal Smoothing Principle in Density Estimation. Annals of Statistics 85, 470–477 (1990)

    MathSciNet  Google Scholar 

  4. Fan, J., Gijbels, I.: Variable Bandwidth and Local Linear Regression Smoothers. Annals of Statistics 20, 2008–2036 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  5. Terrell, G.R., Scott, D.W.: Variable Kernel Density Estimation. Annals of Statistics 20, 1236–1265 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  6. Turlach, B.A.: Bandwidth Selection in Kernel Density Estimation: A Review. Universite Catholique de Louvain, Technical report (1993)

    Google Scholar 

  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. 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. Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  11. Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10, 215–236 (1996)

    Article  Google Scholar 

  12. Friedman, J.H.: Multivariate Adaptive Regression Splines. Annals of Statistics 19, 1–141 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  13. Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, Boca Raton (1990)

    MATH  Google Scholar 

  14. Michalak, M., Sta̧por, K.: Estymacja ja̧drowa w predykcji szeregów czasowych. Studia Informatica 29 3A (78), 71–90 (2008)

    Google Scholar 

  15. Michalak, M.: Możliwości poprawy jakości usług w transporcie miejskim poprzez monitoring natȩżenia potoków pasażerskich. ITS dla Śla̧ska, Katowice (2008)

    Google Scholar 

  16. Sikora, M., Kozielski, M., Michalak, M.: Innowacyjne narzȩdzia informatyczne analizy danych. Wydział Transportu, Gliwice (2008)

    Google Scholar 

  17. de Boor, C.: A practical guide to splines. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  18. Gajek, L., Kałuszka, M.: Wnioskowanie statystyczne, WNT, Warszawa (2000)

    Google Scholar 

  19. Koronacki, J., Ćwik, J.: Statystyczne systemy ucza̧ce siȩ. WNT, Warszawa (2005)

    Google Scholar 

  20. Kulczycki, P.: Estymatory ja̧drowe w analizie systemowej. WNT, Warszawa (2005)

    Google Scholar 

  21. Box, G.E.P., Jenkins, G.M.: Analiza szeregów czasowych. PWN, Warszawa (1983)

    Google Scholar 

  22. Gasser, T., Muller, H.G.: Estimating Regression Function and Their Derivatives by the Kernel Method. Scandinavian Journal of Statistics 11, 171–185 (1984)

    MATH  MathSciNet  Google Scholar 

  23. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)

    Book  MATH  Google Scholar 

  24. Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probability and Its Applications 14, 153–158 (1969)

    Article  Google Scholar 

  25. Nadaraya, E.A.: On estimating regression. Theory of Probability and Its Applications 9, 141–142 (1964)

    Article  Google Scholar 

  26. Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)

    Google Scholar 

  27. Taylor, J.S., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  28. Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1988)

    Google Scholar 

  29. Watson, G.S.: Smooth Regression Analysis. Sankhya - The Indian Journal of Statistics 26, 359–372 (1964)

    MATH  Google Scholar 

  30. Cleveland, W.S., Devlin, S.J.: Locally Weighted Regression. Jour. of the Am. Stat. Ass. 83, 596–610 (1988)

    Article  MATH  Google Scholar 

  31. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, Boca Raton (1995)

    Book  MATH  Google Scholar 

  32. Smola, A.J.: Regression Estimation with Support Vector Learning Machines. Technische Universität München (1996)

    Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Michalak, M. (2009). Time Series Prediction Using New Adaptive Kernel Estimators. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-93905-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93904-7

  • Online ISBN: 978-3-540-93905-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics