Comparison of Linear Regression and Neural Network Models Forecasting Tourist Arrivals to Turkey
Keywords:
Tourism Forecasting, Tourism Demand Modeling, Time Series, Linear Regression, Neural Networks, Multilayer Perceptron, Multivariate Tourism ForecastingAbstract
This paper develops statistical and machine learning methods for estimating tourist arrivals which is one of the donnée for planning the sustainable tourism development. Tourism is arguably one of the world's largest and fastest growing industries. Sustainable tourism development is one of the most promising generators of the sustainable economic development. Realistic tourism projections based on accurate tourism forecasting contribute much for the sustainable tourism development. The challenge of the planning and
developing sustainable tourism is to see as the complex paradigm but one of the starting points is the accurate forecasting tourist arrivals. In this study, linear regression and neural network multilayer perceptron (MLP) implementations are considered to make multivariate tourism forecasting for Turkey. Comparison of forecasting performances in terms of correlation coefficient (R), relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that MLP model for regression gives a better
performance.
References
Al-Saba, T., & El-Amin, I. (1999). Artificial neural networks as applied to long-term demand
forecasting. Artificial Intelligence in Engineering, 13(2), 189-197.
Archer, B. (1987). Demand forecasting and estimation, in Brent Ritche, J R and Goeldner, C R
(eds) Travel Tourism and Hospitality Research: A Handbook for Managers and
Researchers, New York: Wiley.
Aslan, A., Kaplan, M., & Kula, F. (2008). Approach, International Tourism Demand for Turkey: A
Dynamic Panel Data. Munich Personal RePEc Archive MPRA Paper No. 10601.
Aslanargun, A., Mammadov, M., Yazici, B., & Yolacan, S. (2007). Comparison of ARIMA, neural
networks and hybrid models in time series: tourist arrival forecasting. Journal of Statistical
Computation and Simulation, 77(1), 29–53.
Ayata, T., Cam, E., & Yıldız, O. (2007). Adaptive neuro-fuzzy inference systems (ANFIS)
application to investigate potential use of natural ventilation in new building designs in
Turkey. Energy Conversion and Management, 48, 1472–1479.
Beccali, M., Cellura, M., Lo Brano, V., & Marvuglia, A. (2004). Forecasting daily urban electric
load profiles using artificial neural networks. Energy Conversion and Management.
Bishop, C. (1995). Neural Networks for Pattern Recognition.Oxford: Oxford University Press.
Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of an intelligent supplier relationship
management system: A hybrid case based neural network approach. Expert Systems with
Applications, 24, 225–237.
Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting
with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert
Systems with Applications, 36, 6697–6707.
Gooijer, J. G., & Hyndman, J. R. (2006). 25 years of time series forecasting. International Journal
of Forecasting 22, 443– 473.
H.Witten, I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques
(Second b.). New York: Elsevier.
Haykin, S. (1999). Neural Networks: a comprehensive foundation. (Second Edition b.). Prentice
Hall.
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load
forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16, 44–55.
Hobbs, B. F., Helman, U., Jitprapaikulsarn, S., Konda, S., & Maratukulam, D. (1998). Artificial
neural networks for short-term energy forecasting: Accuracy and economic value.
Neurocomputing, 23, 71–84.
Jones, M. T. (2008). Artificial Intelligence: A Systems Approach. Infınıty Science Press LLC.
Law, R. (2000). Back-propagation learning in improving the accuracy of neural network-based
tourism demand forecasting. Tourism Management, 21(4), 331-340.
Law, R., & Au, N. (1999). A Neural Network Model to Forecast Japanese Demand for Travel to
Hong Kong. Tourism Management, 20(1), 89-97.
Mark, E. F., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA Data Mining Software: An Update; SIGKDD Explorations. 11(1).
Ministry of Culture, T. (2007). Tourism strategy of Turkey – 2023. Ankara: Republic of Turkey of
Ministry of Culture & Tourism.
Pai, P. F., & Hong, W. (2005). An Improved Neural Network Model in Forecasting Arrivals.
Annals of Tourism Research, 32(4), 1138–1141.
Palmer, A., Montano, J. J., & Sese, A. (2006). Designing an artificial neural network for
forecasting tourism time series. Tourism Management , 27, 781–790.
Pattie, D., & Snyder, J. (1996). Using a Neural Network to Forecast Visitor Behavior. Annals of
Tourism Research. 23(1), 153-164.
Petropoulos, C., Patelis, A., Nikolopoulos, K., & Assimakopoulos, V. (2005). A technical analysis
approach to tourism demand forecasting. Applied Economics Letters, 12 (6), 327-333.
Reinsel, G. C. (2003). Elements of multivariate time series analysis. New York: Springer.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by
error propagation. Parallel distributed processing (pp. 318–362). Cambridge, MA: MIT
Press.
Sabuncuoglu, I. (1998). Scheduling with neural networks: A review of the literature and new
research directions. Production Planning and Control, 9(1), 2–12.
Sozen, A., Arcaklioglu, E., & Ozkaymak, M. (2005). Turkey’s net energy consumption. Applied
Energy, 81(2), 209–221.
Vellido, A., Lisboa, P. J., & Vaughan, J. (1999). Neural networks in business: A survey of
applications (1992–1998). Expert Systems with Applications, 17, 51–70.
Witt, S. F., & Witt, C. A. (1995, 3). Forecasting tourism demand: a review of empirical research,
International Journal of Forecasting, 13, 319-327.
Wong, B. K., Lai, S. V., & Lam, J. (2000). A bibliography of neural network business applications
research: 1994–1998. Computers & Operations Research, 27, 1045–1076.
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial networks: The state of
the art. International Journal of Forecasting, 14, 35– 62.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Eurasian J. Sci. Eng is distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY-4.0) https://creativecommons.org/licenses/by/4.0/
