Forecasting Tourist Arrivals Five-Star Hotel in Antalya, Istanbul and Mugla

Abstract

FORECASTING TOURIST ARRIVALS FIVE-STAR HOTEL IN ANTALYA, ISTANBUL AND MUGLA

Öz

This is a comparative study for the provinces of Antalya, Istanbul and Mugla which has the highest tourist overnight stay numbers of Turkey by time series analyses performed by Linear and MLP regression analyses methods. Annual data range has been used to estimate the tourism demand. Multivariate data have been used. WEKA 3.8 data mining software was used in this study where the estimation methods were applied. In some cases, estimation work has been done on the number of foreign tourists. These results were compared with 2 different regression methods. Different regression analysis methods gave the best results for different tourism destinations in forecasting studies. Determined that the regression analysis that gives the best result for the destination of the forecasting study is determined and the closest values can be reached by the regression analysis which gives the result suitable for the destination.

Anahtar Kelimeler

• Accommodation Estimates
• Tourism Forecasting
• Time Series Regression

Kaynakça

1. Wu, D.,Song, H., &Shen, S. (2016). New Developments in Tourism and Hotel Demand Modeling and Forecasting. International Journal of Contemporary Hospitality Management.
2. Koupriouchina, L.,van der Rest, J. P., &Schwartz, Z. (2014). On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management, 41, 104-114.
3. Lim, C.,Chang, C., &McAleer, M. (2009). Forecasting h (m) otel guestnights in New Zealand. International Journal of Hospitality Management, 28(2), 228-235.
4. Cortés-Jiménez, I.,&Blake, A. (2011). Tourism demand modeling by purpose of visit and nationality. Journal of Travel Research, 50(4), 408-416.
5. Smeral, E. (2010). Impacts of the world recession and economic crisis on tourism: Forecasts and potential risks. Journal of Travel Research, 49(1), 31-38.
6. Athanasopoulos, G.,&Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19-31.
7. Baggio, R.,&Sainaghi, R. (2016). Mapping time series in to networks as a tool to assess the complex dynamics of tourism systems. Tourism Management, 54, 23-33.
8. Guizzardi, A.,&Stacchini, A. (2015). Real-time forecasting regional tourism with business sentiment surveys. Tourism Management, 47, 213-223.

9. Falk, M. (2014). Impact of weather conditions on tourism demand in the peak summer season over the last 50 years. Tourism Management Perspectives, 9, 24-35.
10. Song, H., Lin, S., Witt, S. F., &Zhang, X. (2011). Impact of financial/economic crisis on demand for hotel rooms in Hong Kong. Tourism Management, 32(1), 172-186.
11. Wu, E. H.,Law, R., &Jiang, B. (2010). Data mining for hotel occupancy rate: an independent component analysis approach. Journal of Travel &Tourism Marketing, 27(4), 426-438.
12. Chen, C. F.,Lai, M. C., &Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-BasedSystems, 26, 281-287.
13. Claveria, O.,&Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220-228.