Abstract
Empirical Mode Decomposition (EMD), a computational intelligence method dealt with non-linear complex system, was introduced to find out the fluctuation rule of international tourist arrivals in this paper. Main results show that: (1) Through decomposition of long-term and short-term historical data respectively, four IMFs and one residual trend term are both obtained. There are almost the uniform fluctuation periods of the first three IMFs, which are 3, 6, and 12 months separately (Tab.1 and 2). (2) In the long run, the dominant factor to control the change of international tourist arrivals is the residual res whose variance contribution is 83.1%; while in the short term, intense fluctuation of 3 months’ period with the biggest variance contribution, 50%, is still the main change characteristic. (3) Intense fluctuation of international tourist arrivals should be paid more careful consideration when establishing recent tourism plan. At the same time, long-term measures to deal with the large tourists flow will also be endeavored in immediately. As one of the best methods of extracting data series, EMD is great beneficial to predict future international tourist arrivals and provide a theoretical guidance for tourism policy.
This work is supported by Dr. Start-up Fund in Jingling Institute of Technology (jit-b- 201011).
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References
Lei, K.W., Chen, Y.: Forecast of inbound tourists to China based on BP neural network and APIMA combined model. Tourism Tribune 22(4), 20–25 (2007)
Wu, J.H., Ge, Z.S., Yang, D.Y.: An artificial neural network to forecast the international tourism demand– – Taking the Japanese demand for travel to Hongkong as an example. Tourism Tribune 17(3), 55–59 (2002)
Huang, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the Hibert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London 45(4), 903–955 (1998)
Huang, N.E., Shen, Z., Long, S.R.: A new view of nonlinear water waves: the Hibert spectrum. Annual Review of Fluid Mechanics 31, 417–457 (1999)
Chen, L.L., Lin, Z.S., Guo, J.: Research on Chinese future grain security based on the method of EMD. Scientia Agricultura Sinica 42(1), 180–188 (2009)
Zhang, Z.Z., Lin, Z.S., Du, J.L.: Analysis on multi-scale cycles of solar activity with the data of tree-ring (1511-1954). Scientia Geographica Sinica 29(5), 709–714 (2009)
Zhang, Y.G., Lin, Z.S., Liang, R.J.: EMD-based prediction on dunamics of land carrying capacity in Shangdong province. Scientia Geographica Sinica 28(2), 219–223 (2008)
Huang, D.J., Zhao, J.P., Su, J.L.: Practical implementation of the Hilbert-Huang transform algorithm. Acta Oceanologica Sinica 25(1), 1–11 (2003)
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Chen, L., Lin, Z. (2011). New Application of Computational Intelligence Method of EMD. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23223-7_30
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DOI: https://doi.org/10.1007/978-3-642-23223-7_30
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