APPLYING THE EXPONENTIAL SMOOTHING MODEL FOR FORECASTING TOURISTS’ ARRIVALS – EXAMPLE OF NOVI SAD, BELGRADE AND NIŠ

  • Aleksandra Vujko, PhD Novi Sad School of Business, Novi Sad http://orcid.org/0000-0001-8684-4228
  • Nataša Papić-Blagojević, PhD Novi Sad School of Business, Novi Sad
  • Tamara Gajić Novi Sad School of Business, Novi Sad

Abstract

Predicting future movements of tourism demand based solely on the past behaviour of variables such as number of overnight stays is crucial for the development of tourism and mitigation of seasonality. Nowadays, there are many different models that could be used for forecasting. Sometimes, some simpler models could ft better to collected data and, in the other hand, more sophisticated ones are more convenient. 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. Based on the selected data, forecasting was made for overnight stays until May 2018. It is concluded that the selected models correspond to the observed data, and the precision of the obtained predictions is determined by comparing the BIC precision measures.

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References

1. Butler, R., & Mao, B. (1997). Seasonality in Tourism: Problems and Measurement. In P. Murphy (ed.), Quality Management, Chichester, New York, Wiley
2. Butler, R. W. (1994). Seasonality in tourism: issues and implication. In A. V. Seaton (Ed.), Tourism: A state of the art (pp. 332-339). Chichester, UK: Wiley.
3. Brown, R. G. (1959). Statistical forecasting for inventory control, New York: McGraw-Hill.
4. Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24, 323–330.
5. Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220-228.
6. Coshall, J.T. (2009). Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management, 30, 495–511.
7. Coshall, J.T., & Charlesworth, R. (2011) A management orientated approach to combination forecasting of tourism demand. Tourism Management, 32, 759- 769.
8. Cuccia, T., & Rizzo, I. (2011). Tourism seasonality in cultural destinations: Empirical evidence from Sicily. Tourism Management, 32, 589-595.
9. Everette S., & Gardner Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting. 4(1), 1-28.
10. Everette S., & Gardner Jr. (2006). Exponential smoothing: The state of the art—Part II. International journal of Forecasting. 22(4), 637-666.
11. Gounopoulos, D., Petmezas, D., & Santamaria, D. (2012). Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists origin. Annals of Tourism Research, 39 (2), 641-666.
12. Guzman-Para, V.F., Quintana-García, C., Benavides-Velasco, C.A., & VilaOblitas, J.B. (2015). Trends and seasonal variation of tourist demand in Spain: The role of rural tourism. Tourism Management Perspectives, 16, 123–128.
13. Gajić, T., Vujko, A., & 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.
14. Hassani, H., Webster, A., Silva, E.S., & Heravi, S. (2015). Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis, Tourism Management, 46, 322-335.
15. Holt, C. C. (1957). Forecasting trends and seasonals by exponentially weighted averages, O.N.R. Memorandum 52/1957, Carnegie Institute of Technology.
16. Lim, C., & McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4), 965-977.
17. Maia, A.L.S., & Carvalho, F.A.T. (2011). Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting, 27, 740–759.
18. Onder, I., & Gunter, U. (2015). Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tourism management, 46, 123–135.
19. Peng, L.Z., Hong, Y., Cai, L.Y., & Qiang, L.F. (2008). An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street. Acta Automatica Sinica, 34(11), 1404-1409.
20. Papić-Blagojević, N., Vujko, A., & Gajić, T. (2016). Comparative analysis of exponential smoothing models to tourists’ arrivals in Serbia. Economic of agriculture. 63(3), 835-847.
21. Ursache, M. (2015). Tourism – signifcant driver shaping a destinations heritage. Procedia - social and Behavioral Sciences, 188(14), 130-137.
22. Vallet, A.C., Bermudes, J.D., & Vercher, E. (2011). Forecasting correlated time series with exponential smoothing models. International Journal of Forecasting, 27, 252–266.
23. Vujko, A., & Gajić, T. (2014). The gouverment policy impact on economic development of tourism. Economic of agriculture, 61(3), 789-804.
24. Witt, C., Witt, S., & Wilson, N. (1994). Forecasting international tourist flows. Annals of Tourism Research, 21(3), 612-628.
25. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving average, Management Science, 6, 324-342.
26. Yaffee, R. A., & McGee, M. (2000). Introduction to time series analysis and forecasting: With Applications in SAS and SPSS. San Diego: Academic Press.
Published
2018-06-27
How to Cite
VUJKO, Aleksandra; PAPIĆ-BLAGOJEVIĆ, Nataša; GAJIĆ, Tamara. APPLYING THE EXPONENTIAL SMOOTHING MODEL FOR FORECASTING TOURISTS’ ARRIVALS – EXAMPLE OF NOVI SAD, BELGRADE AND NIŠ. Economics of Agriculture, [S.l.], v. 65, n. 2, p. 757-767, june 2018. ISSN 2334-8453. Available at: <http://ea.bg.ac.rs/index.php/EA/article/view/502>. Date accessed: 25 sep. 2018. doi: https://doi.org/10.5937/ekoPolj1802757V.