COMPARATIVE ANALYSIS OF EXPONENTIAL SMOOTHING MODELS TO TOURISTS ARRIVALS IN SERBIA
DOI:
https://doi.org/10.5937/ekoPolj1603835PKeywords:
time series forecasting, exponential smoothing models, tourist arrivals, SerbiaAbstract
Seasonality is one of the main aspects affecting tourism. Considering the rapid increase in international tourism demand over the last few decades, predictions of future trends of tourism demand are of particular importance for the Government and the economy. We analyze the seasonality of tourist presence in different cities in Serbia. In this paper, the exponential smoothing models have been applied on the data that was taken from Republic Statistical Offce (RSO). The research was conducted on monthly data relating to the number of overnight stays in Belgrade, Novi Sad and Niš during the period from January 2000 to December 2013. The precision of the obtained predictions is determined by comparing the RMSE and BIC precision measures. Based on the selected data, forecasting was made and it is concluded that the selected models correspond to the observed data very well.
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References
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2. Brown, R. G. (1959): Statistical forecasting for inventory control, New York: McGraw-Hill.
3. Cuccia, T. and Rizzo, I. (2011): Tourism seasonality in cultural destinations: Empirical evidence from Sicily, Tourism Management, Vol. 32, pp. 589-595.
4. Cho, V. (2003): A comparison of three different approaches to tourist arrival forecasting, Tourism Management, Vol. 24, pp. 323–330.
5. Coshall, J.T. (2009): Combining volatility and smoothing forecasts of UK demand for international tourism, Tourism Management, Vol. 30, pp. 495–511.
6. Coshall, J.T. and Charlesworth, R. (2011): A management orientated approach to combination forecasting of tourism demand, Tourism Management, Vol. 32, pp. 759-769.
7. de Oliveira, J. A. P. (2003): Government responses to tourism development: Three Brazilian case studies, Tourism Management, Vol. 24, pp. 97–110.
8. Gajić, T., Vujko, A. and Papić Blagojević, N. ( 2015): Forecasting tourist arrivals in Novi Sad by using the ARIMA model, Second International Conference "Higher education in function of development of tourism in Serbia and Western Balkans", In Proceedings, Business Technical College, Užice, pp. 137-146.
9. Gounoploulos, D., Petmezas, D. and Santamaria, D. (2012): Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists origin, Annals of Tourism Research, Vol. 39, Issue 2, pp. 641-666.
10. Hassani, H., Webster, A., Silva, E.S. and Heravi, S. (2015): Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis, Tourism Management, Vol. 46, pp. 322-335.
11. Holt, C. C. (1957): Forecasting trends and seasonals by exponentially weighted averages, O.N.R. Memorandum 52/1957, Carnegie Institute of Technology.
12. Koehler, A.B., Snyder, D.R., Ord, K.J. (2001): Forecasting models and prediction intervals for the multiplicative Holt–Winters method, International Journal of Forecasting, Vol. 17, Issue 2, pp. 269-286.
13. Lim, C. and McAleer, M. (2001): Forecasting tourist arrivals, Annals of Tourism Research, Vol. 28, Issue 4, pp. 965-977.
14. Lin, C.J. and Lee, T.S. (2013): Tourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artifcial Neural Network and Support Vector Regression, Advances in Management & Applied Economics, Vol. 3, Issue 6, pp. 1-18.
15. Nunkoo, R., and Smith, S. L. J. (2013): Political economy of tourism: Trust in government actors, political support, and their determinants, Tourism management, Vol. 36, pp. 120–132.
16. Song, H. and Li, G. (2008): Tourism demand modelling and forecasting-A review of recent research, Tourism Management, Vol. 29, pp. 203–220.
17. Songa, H., Lib, G., Wittb, S.F., Athanasopoulosc, G. (2011): Forecasting tourist arrivals using time-varying parameter structural time series models, International Journal of Forecasting, Vol. 27, pp. 855–869.
18. Sudheer, G., Suseelatha, A. (2015): Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models. International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 340-346.
19. Vallet, A.C., Bermudes, J.D. and Vercher, E. (2011): Forecasting correlated time series with exponential smoothing models, International Journal of Forecasting, Vol. 27, pp. 252–266.
20. Vujko, A., Gajić, T. (2014): The gouverment policy impact on economic development of tourism, Ekonomika poljoprivrede., Vol. 61, Issue 3, pp. 789-804.
21. Winters, P. R. (1960): Forecasting sales by exponentially weighted moving average, Management Science, Vol. 6, pp. 324-342.
22. Zhi-Peng, L., Hong, Y., Yun-Cai, L., Fu-Qiang, L. (2008): An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street, Acta Automatica Sinica, Vol. 34, No. 11, pp. 1404-1409.
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Published
2016-08-31
How to Cite
Papić-Blagojević, N., Vujko, A., & Gajić, T. (2016). COMPARATIVE ANALYSIS OF EXPONENTIAL SMOOTHING MODELS TO TOURISTS ARRIVALS IN SERBIA. Economics of Agriculture, 63(3), 835–845. https://doi.org/10.5937/ekoPolj1603835P
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