A hybrid model by empirical mode decomposition and support vector regression for tourist arrivals forecasting

Journal of Testing and Evaluation

Volumn 41, Issue 3, 2013, Pages

  Lai, Ming Cheng ^a   Yeh, Ching Chiang ^a   Shieh, Lon Fon ^b  

^a NATIONAL TAIPEI UNIVERSITY OF BUSINESS   (Taiwan)

^b NATIONAL UNITED UNIVERSITY   (Taiwan)

Author keywords

Empirical mode decomposition; Hilbert Huang transform; Support vector regression; Tourist arrivals

Indexed keywords

EMPIRICAL MODE DECOMPOSITION; FINITE SET; HILBERT HUANG TRANSFORMS; HYBRID MODEL; INTRINSIC MODE FUNCTIONS; REAL DATA SETS; SUPPORT VECTOR REGRESSION (SVR); TOURIST ARRIVALS;

FORECASTING; REGRESSION ANALYSIS;

SIGNAL PROCESSING;

EID: 84878299314     PISSN: 00903973     EISSN: None     Source Type: Journal    
DOI: 10.1520/JTE20120120     Document Type: Article

References (50)

1. Archer, B. H., "Demand Forecasting and Estimation." Travel, Tourism and Hospitality Research: A Handbook for Managers and Researchers, J. R. B. Ritchie and C. R. Goeldner, Eds., John Wiley, New York, 1987.
2. Yu, G. and Schwartz, Z., "Forecasting Short Time-Series Tourism Demand With Artificial Intelligence Models," J. Travel Res., Vol. 45, 2006, pp. 194-203.
3. Li, G., Song, H., and Witt, S. F., "Recent Developments in Econometric Modeling and Forecasting," J. Travel Res., Vol. 44, 2005, pp. 82-99.
4. Lim, C. and McAleer, M., "Forecasting Tourist Arrivals," Ann. Tourism Res., Vol. 28(4), 2001, pp. 965-977.
5. Botha, I. and Saayman, A., "Time Varying Parameter Error Correction Model Approach to Forecasting Tourist Arrivals in South Africa," Studies Econ. Economet., Vol. 36(1), 2012, pp. 23-42.
6. Nowman, K. B. and Van Dellen, S., "Forecasting Overseas Visitors to the UK Using Continuous Time and Autoregressive Fractional Integrated Moving Average Models with Discrete Data," Tour. Econ., Vol. 18(4), 2012, pp. 835-844.
7. Song, H., Witt, S. F., and Jensen, T. C., "Tourism Forecasting: Accuracy of Alternative Economic Models," Int J. Forecast, Vol. 19, 2003, pp. 123-141.
8. Witt, S. F., Song, H., and Louvieris, P., "Statistical Testing in Forecasting Model Selection," J. Travel Res., Vol. 4(2), 2003, pp. 151-158.
9. Wong, K. F., "Introduction: Tourism Forecasting State of the Art," J. Travel Tourism Market., Vol. 13(1/2), 2002, pp. 1-3.
10. Law, R., "Back-Propagation Learning in Improving the Accuracy of Neural Network-Based Tourism Demand Forecasting," Tourism Manage., Vol. 21, 2000, pp. 331-340.
11. Lee, C. G., "Are Tourist Arrivals Stationary? Evidence From Singapore," Int. J. Tourism Res., Vol. 11(4), 2009, pp. 409-414.
12. Weigend, A. S. and Gershenfeld, N. A., Time Series Prediction: Forecasting the Future and Understanding the Past, Addison-Wesley, Boston, MA, 1994.
13. Law, R. and Au, N., "A Neural Network Model to Forecast Japanese Demand for Travel to Hong Kong," Tourism Manage., Vol. 20, 1999, pp. 89-97.
14. Cao, L. J., "Support Vector Machines Experts for Time Series Forecasting," Neurocomputing, Vol. 51, 2003, pp. 321-339.
15. Cao, L. J. and Tay, F. E. H., "Financial Forecasting Using Support Vector Machines," Neural Comput. Appl., Vol. 10, 2001, pp. 184-192.
16. Tay, F. E. H. and Cao, L. J., "Application of Support Vector Machines in Financial Time Series Forecasting," Omega, Vol. 29, 2001, pp. 309-317.
17. Tay, F. E. H. and Cao, L. J., "Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting," IEEE Trans. Neural Net., Vol. 14, 2003, pp. 1506-1518.
18. Kon, S. C. and Turner, W. L., "Neural Network Forecasting of Tourism Demand," Tourism Econ., Vol. 11, 2005, pp. 301-328.
19. Song, H. and Li, G., "Tourism Demand Modeling and Forecasting - A Review of Recent Research," Tourism Manage., Vol. 29, 2008, pp. 203-220.
20. Vapnik, V. N., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
21. Vapnik, V. N., "An Overview of Statistical Learning Theory," IEEE Trans. Neural Net., Vol. 10, 1999, pp. 988-999.
22. Vapnik, V. N., The Nature of Statistical Learning Theory, 2nd ed., Springer-Verlag, New York, 2000.
23. Pai, P. F. and Lin, C. S., "Using Support Vector Machines in Forecasting Production Values of Machinery Industry in Taiwan," Int. J. Adv. Manuf. Technol., Vol. 27, 2005, pp. 205-210.
24. Mohandes, M. A., Halawani, T. O., Rehmam, S., and Hussain, A. A., "Support Vector Machines for Wind Speed Prediction," Renew. Energy, Vol. 29, 2004, pp. 939-947.
25. Ince, H. and Trafalis, T. B., "A Hybrid Model for Exchange Rate Prediction," Decis. Support Syst., Vol. 42, 2006, pp. 1054-1062.
26. Hong, W.-C., Dong, Y., Zhang, W. Y., Chen, L.-Y., and Panigrahi, B. K., "Cyclic Electric Load Forecasting by Seasonal SVR With Chaotic Genetic Algorithm," Int. J. Electr. Power Energy Syst., Vol. 44(1), 2013, pp. 604-614.
27. Manjula, M., Mishra, S., and Sarma, A. V. R. S., "Empirical Mode Decomposition With Hilbert Transform for Classification of Voltage Sag Causes Using Probabilistic Neural Network," Int. J. Electr. Power Energy Syst., Vol. 44(1), 2013, pp. 597-603.
28. Pai, P. F. and Hong, W. C., "Support Vector Machines With Simulated Annealing Algorithms in Electricity Load Forecasting," Energy Conv. Manage., Vol. 46, 2005, pp. 2626-2669.
29. Pai, P. F., Hong, W. C., and Lin, C. S., "Forecasting Tourism Demand Using a Multifactor Support Vector Machine Model," Lecture Notes Comput. Sci., Vol. 3801, 2005, pp. 512-519.
30. Hong, W. C., Dong, Y., Chen, L. Y., and Wei, S. Y., "SVR With Hybrid Chaotic Genetic Algorithms for Tourism Demand Forecasting," Appl. Soft. Comput., Vol. 11(2), 2011, pp. 1881-1890.
31. Chen, K. Y. and Wang, C. H., "Support Vector Regression With Genetic Algorithms in Forecasting Tourism Demand," Tourism Manage., Vol. 28(1), 2007, pp. 215-226.
32. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., and Zheng, Q., "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis," Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, Series A, Vol. 454, 1998, pp. 903-995.
33. Hwang, P. A., Huang, N. E., and Wang, D. W., "A Note on Analyzing Nonlinear and Non-Stationary Ocean Wave Data," Appl. Ocean Res., Vol. 25(4), 2003, pp. 187-193.
34. Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., and Varanini, M., "Deriving the Respiratory Sinus Arrhythmia from the Heartbeat Time Series Using Empirical Mode Decomposition," Chaos Soliton Fract., Vol. 20(1), 2004, pp. 171-177.
35. Cheng, J. S., Yu, D. J., and Yang, Y., "Energy Operator Demodulating Approach Based on EMD and Its Application in Mechanical Fault Diagnosis," Chinese J. Mech. Eng., Vol. 40(8), 2004, pp. 115-118.
36. Huang, J., Hu, X., and Geng, X., "An Intelligent Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EMD Energy Entropy and Multi-Class Support Vector Machine," Electr. Power Syst. Res., Vol. 81, 2011, pp. 400-407.
37. Rai, V. K. and Mohanty, A. R., "Bearing Fault Diagnosis Using FFT of Intrinsic Mode Functions in Hilbert-Huang Transform," Mech. Syst. Signal Proc., Vol. 21(6), 2007, pp. 2607-2615.
38. Wu, J. D. and Liao, S. Y., "A Self-Adaptive Data Analysis for Fault Diagnosis of an Automotive Air-Conditioner Blower," Exp. Syst. Appl., Vol. 38, 2011, pp. 545-552.
39. Blanco-Velasco, M., Weng, B., and Barner, K. E., "ECG Signal De-Noising and Baseline Wander Correction Based on the Empirical Mode Decomposition," Comput. Biol. Med., Vol. 38(1), 2008, pp. 1-13.
40. Li, H. G. and Meng, G., "Detection of Harmonic Signals from Chaotic Interference by Empirical Mode Decomposition," Chaos Soliton Fract., Vol. 30(4), 2006, pp. 930-935.
41. An, X., Jiang, D., Zhao, M., and Liu, C., "Short-Term Prediction of Wind Power Using EMD and Chaotic Theory," Commun. Nonlinear Sci. Numer. Simul., Vol. 17(2), 2012, pp. 1036-1042.
42. Guo, Z., Zhao, W., Lu, H., and Wang, J., "Multi-Step Forecasting for Wind Speed Using a Modified EMD-Based Artificial Neural Network Model," Renew. Energy, Vol. 37(1), 2012, pp. 241-249.
43. Dong, Y., Li, Y., Xiao, M., and Lai, M., "Analysis of Earthquake Ground Motions Using an Improved Hilbert-Huang Transform," Soil Dyn. Earthq. Eng., Vol. 28(1), 2008, pp. 7-19.
44. Chen, K.-L., Yeh, C.-C., and Lu, T.-L., "A Hybrid Demand Forecasting Model Based on Empirical Mode Decomposition and Neural Network in TFT-LCD Industry," Cybernet. Syst.: Int. J., Vol. 43(5), 2012, pp. 426-441.
45. Lin, C.-S., Chiu, S.-H., and Lin, T.-Y., "Empirical Mode Decomposition-Based Least Squares Support Vector Regression for Foreign Exchange Rate Forecasting," Econ. Modell., Vol. 29(6), 2012, pp. 2583-2590.
46. Flandrin, P., Rilling, G., and Goncalves, P., "Empirical Mode Decomposition as a Filter Bank," IEEE Signal Process. Lett., Vol. 11(2), 2004, pp. 112-114.
47. Cherkassky, V. and Ma, Y., "Practical Selection of SVM Parameters and Noise Estimation for SVM Regression," Neural Net., Vol. 17, 2004, pp. 113-126.
48. Lin, C. J., Hsu, C. W., and Chang, C. C., 2003, "A Practical Guide to Support Vector Classification," http://www.csie. ntu.edu.tw/~cjlin (Last accessed 4 May 2012).
49. Martin, C. A. and Witt, S. F., "Forecasting Tourism Demand: A Comparison of the Accuracy of Several Quantitative Methods," Int J. Forecast, Vol. 5, 1989, pp. 7-19.
50. Chang, C. C. and Lin, C. J., 2011, "LIBSVM: A Library for Support Vector Machines," http://www.csie.ntu.edu.tw/ ~cjlin/libsvm (Last accessed 4 May 2012).