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Discrete Dynamics in Nature and Society
Volume 2012, Article ID 431512, 12 pages
Research Article

Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

Department of Management Science and Information System, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

Received 28 August 2012; Accepted 3 October 2012

Academic Editor: Carlo Piccardi

Copyright © 2012 Yukun Bao 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. S. Y. Abed, A. O. Ba-Fail, and S. M. Jasimuddin, “An econometric analysis of international air travel demand in Saudi Arabia,” Journal of Air Transport Management, vol. 7, no. 3, pp. 143–148, 2001. View at Google Scholar
  2. H. Grubb and A. Mason, “Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend,” International Journal of Forecasting, vol. 17, no. 1, pp. 71–82, 2001. View at Google Scholar
  3. DETR, Air Traffic Forecast for the United Kingdom, in Department of the Environment, Transport and the Regions, 2000.
  4. DFT, “The Future of Air Transport,” Department for Transport, 2003.
  5. DFT, Passenger Forecasts: Additional Analysis, Department for Transport, 2003.
  6. DFT, UK Air Passenger Demand and CO2 Forecasts, Department for Transport, 2007.
  7. W. E. O'Connor, An Introduction to Airline Economics, Praeger Publishers, 2001.
  8. J. D. Bermúdez, J. V. Segura, and E. Vercher, “Holt-Winters forecasting: an alternative formulation applied to UK air passenger data,” Journal of Applied Statistics, vol. 34, no. 9-10, pp. 1075–1090, 2007. View at Publisher · View at Google Scholar
  9. K. Nam and T. Schaefer, “Forecasting international airline passenger traffic using neural networks,” Logistics and Transportation Review, vol. 31, no. 3, 1995. View at Google Scholar
  10. N. Kyungdoo, Y. Junsub, and R. P. Victor, “Predicting airline passenger volume,” The Journal of Business Forecasting Methods & Systems, vol. 16, no. 1, pp. 14–16, 1997. View at Google Scholar
  11. L. Yu, S. Wang, and K. K. Lai, “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm,” Energy Economics, vol. 30, no. 5, pp. 2623–2635, 2008. View at Google Scholar
  12. C. F. Chen, M. C. Lai, and C. C. Yeh, “Forecasting tourism demand based on empirical mode decomposition and neural network,” Knowledge-Based Systems, vol. 26, pp. 281–287, 2011. View at Google Scholar
  13. Y. Wei and M. C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research C, vol. 21, no. 1, pp. 148–162, 2012. View at Google Scholar
  14. M. Dätig and T. Schlurmann, “Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves,” Ocean Engineering, vol. 31, no. 14-15, pp. 1783–1834, 2004. View at Google Scholar
  15. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Google Scholar
  16. N. E. Huang, M.-L. C. Wu, S. R. Long et al., “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” The Royal Society of London. Proceedings A, vol. 459, no. 2037, pp. 2317–2345, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  17. S. S. P. Shen, T. Shu, N. E. Huang et al., “HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data,” in Hilbert-Huang Transform and its Applications, vol. 5, pp. 187–209, 2005. View at Publisher · View at Google Scholar
  18. J. Cheng, D. Yu, and Y. Yang, “Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform,” Mechanical Systems and Signal Processing, vol. 21, no. 3, pp. 1197–1211, 2007. View at Google Scholar
  19. G. Rilling, P. Flandrin, and P. Gonçalvés, On empirical mode decomposition and its algorithms, 2003.
  20. F. Wu and L. Qu, “An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery,” Journal of Sound and Vibration, vol. 314, no. 3, pp. 586–602, 2008. View at Google Scholar
  21. R. R. Andrawis, A. F. Atiya, and H. El-Shishiny, “Forecast combinations of computational intelligence and linearmodels for the NN5 time series forecasting competition,” International Journal of Forecasting, vol. 27, no. 3, pp. 672–688, 2011. View at Google Scholar
  22. B. Onoz and M. Bayazit, “The power of statistical tests for trend detection,” Turkish Journal of Engineering and Environmental Sciences, vol. 27, no. 4, pp. 247–251, 2003. View at Google Scholar
  23. X. Francis and S. Roberto, “Comparing Predictive Accuracy,” Journal of Business & Economic Statistics, vol. 13, no. 3, 1995. View at Google Scholar
  24. W. J. Conover, Practical Nonparametric Statistics, vol. 3, John Wiley & Sons, New York, NY, USA, 1980.
  25. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, p. 27, 2011. View at Google Scholar
  26. Y. Khandakar and R. J. Hyndman, “Automatic time series forecasting: the forecast Package for R,” Journal of Statistical Software, vol. 27, no. i03, 2008. View at Google Scholar