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A Hybrid of EEMD and LSSVM-PSO model for Tourist Demand Forecasting


  • Department of Mathematics, Science Faculty, University Technology of Malaysia, Skudai, Johor, 81310, Malaysia


In this research, hybrid model of Least Square Support Vector Machine (LSSVM) and Ensemble Empirical Mode Decomposition (EEMD) are presented to forecast tourism demand in Malaysia. Foremost, the original series of tourism arrivals data was separated using EEMD technique into residual and Intrinsic Mode Functions (IMFs) components. Next, both of IMFs and residual components were forecasted using Particle Swarm Optimization (LSSVM–PSO) method. In the end, the predicted result of IMFs and residual components from LSSVM–PSO method are sum together to produce the forecasted value for tourism arrivals in Malaysia. Empirical results showed that the presented model in this paper outperform individual forecasting model. The result indicated that LSSVM–PSO is a promising tool in time series forecasting by having the presence of non-stationary and non-linearity in the time series data.


Article Swarm Optimization Forecasting, Ensemble Empirical Mode Decomposition, Forecasting, Least Square Support Vector Machines, Mutual Information, Tourism Demand.

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  • Uysal M, El Roubi MS. Artificial Neural Networks versus multiple regression in tourism demand analysis. J Trav Res. 1999; 38:111–8.
  • Law R. Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tour Manage. 2000; 21:331–40.
  • Burger C, Dohnal M, Kathrada M, Law R. A practitioner’s guide to time-series methods for tourism demand forecasting: A case study of Durban, South Africa. Tour Manage. 2001; 22:403–9.
  • Law R. The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques. J Trav Tour Mark. 2001; 10(2-3):47–66.
  • Tsaur SH, Chiu YC, Huang CH. Determinants of guest loyalty to international tourist hotels: A neural network approach. Tour Manag. 2002; 23:397–405.
  • Cho V. A comparison of three different approaches to tourist arrival forecasting. Tour Manage. 2003; 24:323–30.
  • Kon SC, Turner WL. Neural network forecasting of tourism demand. Tour Econ. 2005; 11:301–28.
  • Palmer A, Jose Montano JJ, Sese A. Designing an Artificial Neural Network for forecasting tourism time series. Tour Manage. 2006; 27:781–90.
  • Chen KY. Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications. 2011; 38:10368–76.
  • Chen CF, Lai MC, Yeh C. Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems. 2012; 26:281–7.
  • Oscar C, Salvador T. Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling. 2014; 36:220–8.
  • Wang S, Yu L, Tang L, Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy. 2011; 36:6542–54.
  • Suykens JAK, Van Gestel T, De Brabanter J, De Moor J, Vandewalle J. Least squares Support Vector Machines. World Scientific Singapore; 2002.
  • Yang LS, Yang S, Zhang R, Jin H. Sparse Least Square Support Vector Machine via coupled compressive pruning. Neurocomputing. 2014; 131:77–86.
  • Wang H, Hu D. Comparison of SVM and LS-SVM for regression. IEEE International Conference Neural Networks and Brain; 2005. p. 279–83.
  • Tang L, Yu L, Wang S, Li J, Wang S. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Applied Energy. 2012; 93:432–43.
  • Xie G, Wang S, Zhao Y, Lai KK. Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study. Applied Soft Computing. 2013; 13(5):2232–41.
  • Niu D, Kou B, Zhang Y, Gu Z. A short-term load forecasting model based on LS-SVM optimized by dynamic inertia weight Particle Swarm Optimization Algorithm. Advances in Neural Networks. 2009; 5552:242–50.
  • Fupeng T, Chun-Li W. Tourist number forecast of Gansu Province based on LS-SVM. Proceedings of the 2013 Fifth International Conference on Multimedia Information Networking and Security; 2013. p. 789–91.
  • Ani S, Suhartono S. Stream flow forecasting using Least Squares Support Vector Machines. Hydrological Sciences Journal. 2012; 57(7):1275–93.
  • Zhang HG, Zhang S, Yin YX. A novel improved LSSVM algorithm for a real Industrial application. Mathematical Problems in Engineering. 2014; 2014:1–7.
  • Wu Z, Huang N. Ensemble Empirical Mode Decomposition: A noise assisted data analysis method. Adv Adapt Data Anal. 2009; l(1):1–41.
  • Wang S, Yu L, Tang L, Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption fore casting in China. Energy. 2011; 36(11):6542–54.
  • Liu Z, Sun W, Zeng J. A new short-term load forecasting method of power system based on EEMD and SS-PSO. Neural Comput and Applic. 2014; 24:973–83.
  • Meyer PE, Lafitte F, Bontempi G. Minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics. 2008; 9:461.
  • Kennedy J, Eberhart RC. Particle Swarm Optimization. IEEE International Conference on Neural Networks; 1995. p. 1942–8.


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