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The Scientific World Journal
Volume 2014, Article ID 708918, 5 pages
http://dx.doi.org/10.1155/2014/708918
Research Article

Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia
2Statistics Department, Sebha University, Sebha 00218, Libya

Received 15 January 2014; Revised 23 June 2014; Accepted 25 June 2014; Published 22 July 2014

Academic Editor: Mohamed Hanafi

Copyright © 2014 Abobaker M. Jaber et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A: Mathematical, vol. 454, pp. 903–995, 1971. View at Google Scholar
  2. K. Drakakis, “Empirical mode decomposition of financial data,” International Mathematical Forum, vol. 3, no. 25, pp. 1191–1202, 2008. View at Google Scholar · View at MathSciNet
  3. N. E. Huang, M. Wu, W. Qu, S. R. Long, and S. S. P. Shen, “Applications of Hilbert-Huang transform to non-stationary financial time series analysis,” Applied Stochastic Models in Business and Industry, vol. 19, no. 3, pp. 245–268, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Lin, P. Shang, G. Feng, and B. Zhong, “Application of empirical mode decomposition combined with K-nearest neighbors approach in financial time series forecasting,” Fluctuation and Noise Letters, vol. 11, no. 2, Article ID 1250018, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Rezaei, Physiological Synchrony as Manifested in Dyadic Interactions, University of Toronto, 2013.
  6. Y. Deng, W. Wang, C. Qian, Z. Wang, and D. Dai, “Boundary-processing-technique in EMD method and Hilbert transform,” Chinese Science Bulletin, vol. 46, no. 11, pp. 954–960, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Liu, “A novel boundary extension approach for empirical mode decomposition,” in Intelligent Computing, pp. 299–304, Springer, 2006. View at Publisher · View at Google Scholar
  8. A. M. Jaber, M. T. Ismail, and A. M. Altaher, “Empirical mode decomposition combined with local linear quantile regression for automatic boundary correction,” Abstract and Applied Analysis, vol. 2014, Article ID 731827, 8 pages, 2014. View at Publisher · View at Google Scholar
  9. C. D. Blakely, A Fast Empirical Mode Decomposition Technique for Nonstationary Nonlinear Time Series, Elsevier Science, Amsterdam, The Netherlands, 2005.
  10. A. Amar and Z. E. A. Guennoun, “Contribution of wavelet transformation and empirical mode decomposition to measurement of U.S core inflation,” Applied Mathematical Sciences, vol. 6, no. 135, pp. 6739–6752, 2012. View at Google Scholar · View at Scopus
  11. J. Fan and R. Li, “Statistical challenges with high dimensionality: feature selection in knowledge discovery,” http://arxiv.org/abs/math/0602133. View at Scopus
  12. R. Koenker and G. Bassett Jr., “Regression quantiles,” Econometrica, vol. 46, no. 1, pp. 33–50, 1978. View at Google Scholar
  13. P. Chaudhuri, “Nonparametric estimates of regression quantiles and their local Bahadur representation,” The Annals of Statistics, vol. 19, no. 2, pp. 760–777, 1991. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. R. Koenker, P. Ng, and S. Portnoy, “Quantile smoothing splines,” Biometrika, vol. 81, no. 4, pp. 673–680, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  15. K. Yu and M. C. Jones, “Local linear quantile regression,” Journal of the American Statistical Association, vol. 93, no. 441, pp. 228–237, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. D. Ruppert, S. J. Sheather, and M. P. Wand, “An effective bandwidth selector for local least squares regression,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1257–1270, 1995. View at Google Scholar
  17. T. C. M. Lee and V. Solo, “Bandwidth selection for local linear regression: a simulation study,” Computational Statistics, vol. 14, no. 4, pp. 515–532, 1999. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Cai and X. Xu, “Nonparametric quantile estimations for dynamic smooth coefficient models,” Journal of the American Statistical Association, vol. 103, no. 484, pp. 1595–1608, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus