Volume 1, Number 2 (2016)
Year Launched: 2016
Journal Menu
Previous Issues
Why Us
-  Open Access
-  Peer-reviewed
-  Rapid publication
-  Lifetime hosting
-  Free indexing service
-  Free promotion service
-  More citations
-  Search engine friendly
Contact Us
Email:   service@scirea.org
Home > Journals > SCIREA Journal of Mathematics > Archive > Paper Information

Modelling Singapore Tourist Arrivals to Malaysia by Using SVM and ANN

Volume 1, Issue 2, December 2016    |    PP. 210-216    |PDF (340 K)|    Pub. Date: January 11, 2017
169 Downloads     2127 Views  

Rafidah Ali, University Kuala Lumpur Malaysia Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam,81750 Johor Bharu Johor Malaysia
Ani Shabri, Departments of Mathematics, Science Faculty, University of Technology Malaysia, Skudai, Johor Malaysia

The tourism industry is an increasingly important national industry for Malaysia. Government policymakers and business managers pay close attention to the development of the tourism industry. In this study, two machine learning methods, artificial neural network (ANN) and support vector machine (SVM) were utilized to predict the Singapore tourist arrival to Malaysia. This country is neighbouring country for Malaysia and tourists are more flexible to visit Malaysia using rails and road transportations. This paper aims at finding an accurate forecasting model in order to make the tourism industry grow stably. This study uses monthly time series data from 2010 (January) until 2014 (December). The experiment shows that ANN model outperform SVM base on the criteria Root Mean Absolute Error (RMSE).

Support Vector Machine (SVM), Artificial Intelligence (AI), Artificial Neural Network (ANN)

Cite this paper
Rafidah Ali, Ani Shabri, Modelling Singapore Tourist Arrivals to Malaysia by Using SVM and ANN, SCIREA Journal of Mathematics. Vol. 1 , No. 2 , 2016 , pp. 210 - 216 .


[ 1 ] O.Claveria, E.Monte and S.Torra, Tourism demand forecasting with different neural networks models (2013)
[ 2 ] S. Ismail, R. Samsudin and A. Shabri, A Comparison of Time Series Forecasting Using Support Vector Machine and Artificial Neural Network Model (2010)
[ 3 ] O.Claveria, E.Monte and S.Torra, Forecasting tourism demand to Catalonia: Neural networks vs. time series models (2014)
[ 4 ] J.Shawe-Taylor, N.Cristianini, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University (2000)
[ 5 ] K.Kandananond, A Comparison of Various Forecasting Methods for Autocorrelated Time Series (2012)
[ 6 ] A.Suliman, N.Nazri, M.Othman, M.A.Malek, and K.R.K.Mahamud, Artificial Neural Network And Support Vector Machine In Flood Forecasting: A Review (2013)
[ 7 ] P.F. Pai and C.S. Lin Using Support Vector Machine In Forecasting Production Values Of Machinery Industry In Taiwan. International Journal of Manufacturing Technology 27 (12) (2004) 205-210.
[ 8 ] A.Muhannad and M.M.Shotar. The Application Of Time Series Modelling To Some Major Economic Variables. Ph.D Qatar University.
[ 9 ] X.G.Hua, Y.Q.Ni ,J.M. Ko and K.Y. Wong, Modeling Of Temperature–Frequency Correlation Using Combined Principal Component Analysis And Support Vector Regression Technique (2007)
[ 10 ] A.Rafidah and Y.Suhaila, Modeling River Stream Flow Using Support Vector Machine Vol. 315 (2013) pp 602-605

Submit A Manuscript
Review Manuscripts
Join As An Editorial Member
Most Views
by Sergey M. Afonin
2935 Downloads 40130 Views
by Syed Adil Hussain, Taha Hasan Associate Professor
2295 Downloads 19058 Views
by Omprakash Sikhwal, Yashwant Vyas
2366 Downloads 15996 Views
by Munmun Nath, Bijan Nath, Santanu Roy
2263 Downloads 15916 Views
Upcoming Conferences