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

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.

[1]  Ani Shabri,et al.  Streamflow forecasting using least-squares support vector machines , 2012 .

[2]  Haifeng Wang,et al.  Comparison of SVM and LS-SVM for Regression , 2005, 2005 International Conference on Neural Networks and Brain.

[3]  Tian Fupeng,et al.  Tourist Number Forecast of Gansu Province Based on LS-SVM , 2013 .

[4]  Jianping Li,et al.  A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting , 2012 .

[5]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[6]  Ling Tang,et al.  A novel seasonal decomposition based least squares support vector regression ensemble learning appro , 2011 .

[7]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[8]  Oscar Claveria,et al.  Forecasting tourism demand to Catalonia: Neural networks vs. time series models , 2014 .

[9]  Yi-Chang Chiu,et al.  Determinants of guest loyalty to international tourist hotels—a neural network approach , 2002 .

[10]  Chun-Fu Chen,et al.  Forecasting tourism demand based on empirical mode decomposition and neural network , 2012, Knowl. Based Syst..

[11]  Zhihong Gu,et al.  A Short-Term Load Forecasting Model Based on LS-SVM Optimized by Dynamic Inertia Weight Particle Swarm Optimization Algorithm , 2009, ISNN.

[12]  V. Cho.  A comparison of three different approaches to tourist arrival forecasting , 2003 .

[13]  Sen Cheong Kon,et al.  Neural Network Forecasting of Tourism Demand , 2005 .

[14]  Rui Zhang,et al.  Sparse least square support vector machine via coupled compressive pruning , 2014, Neurocomputing.

[15]  Muzaffer Uysal,et al.  Artificial Neural Networks versus Multiple Regression in Tourism Demand Analysis , 1999 .

[16]  Gianluca Bontempi,et al.  minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.

[17]  R. Law.  Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting , 2000 .

[18]  Kin Keung Lai,et al.  Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study , 2013, Appl. Soft Comput..

[19]  Zhigang Liu,et al.  A new short-term load forecasting method of power system based on EEMD and SS-PSO , 2012, Neural Computing and Applications.

[20]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[21]  Rob Law,et al.  The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques , 2001 .

[22]  Rob Law,et al.  A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa , 2001 .

[23]  Albert Sesé,et al.  Designing an artificial neural network for forecasting tourism time series , 2006 .

[24]  Kuan-Yu Chen,et al.  Combining linear and nonlinear model in forecasting tourism demand , 2011, Expert Syst. Appl..