A Hybrid Model by Empirical Mode Decomposition and Support Vector Regression for Tourist Arrivals Forecasting

This study develops a new hybrid model by integrating empirical mode decomposition (EMD) and support vector regression (SVR) for tourist arrivals forecasting. The proposed approach first uses EMD, which can adaptively decompose the complicated raw data into a finite set of intrinsic mode functions (IMFs) and a residue. After identifying the IMF components and residue, they are then modeled and forecasted using SVR. The final forecasting value can be obtained by the sum of these prediction results. Real data sets of international tourist arrivals to Taiwan were used. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches.

[1]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[2]  Norden E. Huang,et al.  A note on analyzing nonlinear and nonstationary ocean wave data , 2003 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Rob Law,et al.  A neural network model to forecast Japanese demand for travel to Hong Kong , 1999 .

[5]  Chew Ging Lee.  Are tourist arrivals stationary? Evidence from Singapore , 2009 .

[6]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[7]  Haiyan Song,et al.  Tourism forecasting: accuracy of alternative econometric models , 2003 .

[8]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[9]  Hongguang Li,et al.  Detection of harmonic signals from chaotic interference by empirical mode decomposition , 2006 .

[10]  Haiyan Lu,et al.  Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .

[11]  Zvi Schwartz,et al.  Forecasting Short Time-Series Tourism Demand with Artificial Intelligence Models , 2006 .

[12]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[13]  Ping-Feng Pai,et al.  Using support vector machines to forecast the production values of the machinery industry in Taiwan , 2005 .

[14]  Cheng-Hua Wang,et al.  Support vector regression with genetic algorithms in forecasting tourism demand , 2007 .

[15]  Theodore B. Trafalis,et al.  A hybrid model for exchange rate prediction , 2006, Decis. Support Syst..

[16]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[17]  Li Yingmin,et al.  Analysis of earthquake ground motions using an improved Hilbert–Huang transform , 2008 .

[18]  K. Nowman,et al.  Forecasting Overseas Visitors to the UK Using Continuous Time and Autoregressive Fractional Integrated Moving Average Models with Discrete Data , 2012 .

[19]  Haiyan Song,et al.  Introduction: Tourism Forecasting: State of the Art , 2002 .

[20]  Jian-Da Wu,et al.  A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower , 2011, Expert Syst. Appl..

[21]  Wei-Chiang Hong,et al.  Forecasting Tourism Demand Using a Multifactor Support Vector Machine Model , 2005, CIS.

[22]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[23]  Michael McAleer,et al.  Forecasting tourist arrivals , 2001 .

[24]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.

[25]  Chiun-Sin Lin,et al.  Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting , 2012 .

[26]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[27]  A. Saayman,et al.  Time Varying Parameter Error Correction Model Approach To Forecasting Tourist Arrivals In South Africa , 2012 .

[28]  Chao Liu,et al.  Short-term prediction of wind power using EMD and chaotic theory , 2012 .

[29]  Haiyan Song,et al.  Statistical Testing in Forecasting Model Selection , 2003 .

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

[31]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[32]  M. Manjula,et al.  Empirical mode decomposition with Hilbert transform for classification of voltage sag causes using probabilistic neural network , 2013 .

[33]  Stephen F. Witt,et al.  Forecasting tourism demand: A comparison of the accuracy of several quantitative methods , 1989 .

[34]  Xiaoguang Hu,et al.  An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine , 2011 .

[35]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[36]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[37]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[38]  Bijaya Ketan Panigrahi,et al.  Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm , 2013 .

[39]  Junsheng Cheng.  ENERGY OPERATOR DEMODULATING APPROACH BASED ON EMD AND ITS APPLICATION IN MECHANICAL FAULT DIAGNOSIS , 2004 .

[40]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

[41]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[42]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[43]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

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

[45]  Ching-Chiang Yeh,et al.  A HYBRID DEMAND FORECASTING MODEL BASED ON EMPIRICAL MODE DECOMPOSITION AND NEURAL NETWORK IN TFT-LCD INDUSTRY , 2012, Cybern. Syst..

[46]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[47]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .