FORECASTING TOURISM DEMAND: A CASE STUDY FOCUSING ON SPANISH TOURIST’ S TRAVEL TO CAPPADOCIA

Year 2020, Issue: 40, 189 - 208, 29.06.2020

Ebrucan İslamoğlu , Nuri Özgür Doğan

https://doi.org/10.30794/pausbed.679682

Abstract

As international tourism demand continually grows, the importance and magnitude of the tourism sector for the economy of the countries increases. Based on the tourism demand, countries want to be prepared and they need to know the future demand. However, it is not always possible to have the knowledge of the actual demand and one can only make forecasts in such cases. This paper deals with forecasting international tourism demand specifically focusing on the Spanish tourist arrivals in Cappadocia region of Turkey. In accordance with this aim, eight forecasting models are used. The results of the analysis for each model is attained and the forecasting accuracy examined. It is seen that Artificial Neural Networks and the Multiple Regression Model outperforms other models. Finally, administrative inferences, confines of the study and instructions for hereafter researches are given in this paper.

Keywords

Spanish Tourist Arrivals, Artificial Neural Networks, Tourism Demand Forecasting, Cappadocia, Multiple Regression

References

• Aksakal, Mina; Arıcıgil Çilan, Çiğdem (2015). A Review of Tourism Demand for Turkey Via Seemingly Unrelated Regression Models, Journal of International Economic and Administrative Reviews, 7 (14), pp. 235-256.
• Aslan, Alper; Kula, Ferit; Kaplan, Muhittin (2009). International Tourism Demand for Turkey: A Dynamic Panel Data Approach, Research Journal of International Studies, 9, pp. 65-73.
• Athanasopoulos, George; Hyndman, Rob J.; Song, Haiyan;Wu, Doris C. (2011). The Tourism Forecasting Competition, International Journal of Forecasting, 27, pp. 822-844.
• Azaklı, Hatice Seda (2012). “In partial fulfilment of the requirements for the degree of master of science in city and regional planning” (Master’ s Thesis), The graduate school of natural and applied sciences of middle east technical university.
• Ozan, Bahar; Baldemir, Ercan (2008). Causality relationship between international trade and international tourism: the case of Turkey. Journal of Dokuz Eylul University Social Sciences, 10 (4), pp. 97-111.
• Banco Mundial [online] (2018). “World Bank national accounts data, and OECD National Accounts data files. GDP (current US$)” Available at http://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=CN. [Accesed August 2018]
• Prosper, Bangwayo-Skeete F.; Skeete, Ryan W. (2015). Can Google Data Improve the Forecasting Performance of Tourist Arrivals? Mixed-Data Sampling Approach, Tourism Management, 46, pp. 454-464.
• Chatfield, Chris (2015). The analysis of time series an introduction, 5th edition, Chapman&Hall/Crc, London, New York.
• Chen, Chun-Fu; Lai, Ming-Cheng; Yeh, Ching-Chiang (2012). Forecasting Tourism Demand Based on Empirical Mode Decomposition and Neural Network, Knowledge-Based Systems, 26, pp. 281-287.
• Chen, Kuan - Yu (2011). Combining Linear and Non-linear Model in Forecasting Tourism Demand, Expert Systems with Applications, 38, pp. 10368-10376.
• Claveria, Oscar; Monte, Enric; Torra, Salvador (2015). Common Trends in International Tourism Demand: Are They Useful to Improve Tourism Predictions?, Tourism Management Perspectives, 16, pp. 116-122.
• Cloveria, Oscar; Torra, Salvador (2014). Forecasting Tourism Demand to Catalonia: Neural Networks vs. Time Series Models, Economic Modelling, 36, pp. 220-228.
• Cooper, John C. B (1999). Artificial neural networks versus multivariate statistics: an application from economics, Journal of Applied Statistics, 26 (8), pp. 909-921,
• Culture and Tourism Ministry (2016). Promotion General Directorate.
• Çeken, Hüseyin (2008). A Theoretical Review of the Impact of Tourism on Regional Development, Journal of Afyon Kocatepe University Faculty of Economics and Administrative Sciences, 10 (2), pp. 293-306.
• Çuhadar, Murat (2013). Modelling and Forecasting of Foreign Tourism Demand for Turkey using MLP, RBF and TDNN Artificial Neural Network Architect, International Journal of Business and Social Research (IJBSR), 4 (3), pp. 12-28.
• Çuhadar, Murat (2014), Foreign Tourism Demand’s Modelling and Estimation For Muğla Province Between The Years 2012 and 2013, International Journal of Economic and Administrative Studies,12.
• Çuhadar, Murat; Cogurcu, Iclal; Kukrer, Ceyda (2014). Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures, International Journal of Business and Social Research (IJBSR), 4 (3), pp. 12-28.
• Çuhadar, Murat; Güngör, İbrahim; Göksu, Ali (2009). Estimation of Tourism Demand by Using Artificial Neural Networks and Comparative Analysis with Time Series Methods: An Application for Antalya Province, Süleyman Demirel University, The Journal of Faculty of Economics and Administrative Sciences, 14 (1), pp. 99-114.
• Çuhadar, Murat; Kervankıran, İsmail (2016). Analysis, Modelling and Estimation of Tourism Demand for Nevşehir Province Accommodation Enterprises, Journal of Economics and Management Research, 5 (2).
• Daştan, Hüseyin; Dudu, Nagehan; Çalmaşur, Gürkan (2016). Winter tourism demand: an application on Erzurum province. Journal of Ataturk University econoics and administrative sciences, 30 (2), 2016.
• Eryiğit, Mehmet; Kotil, Erdoğan; Eryiğit, Resul (2010). Factors Affecting International Tourism Flows toTurkey: A Gravity Approach, Tourism Economics, 16 (3), pp. 585-595.
• Goh, Carey; Law, Rob (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management, 23, pp. 499-510.
• Goh, Carey; Law, Rob (2011). The Methodological Progress of Tourism Demand Forecasting: A Review of Related Literature, Journal of Travel and Tourism Marketing, 28 (3), pp. 296-317.
• Görmüş, Şakir; Göçer, İsmet (2010). The Socio - Economic Determinant of Tourism Demand in Turkey: A Panel Data Approach, International Research Journal of Finance and Economics, 55, pp. 87-98.
• Gunter, Ulrich; Önder, İrem (2015). Forecasting International City Tourism Demand for Paris: Accuracy of uni- and Multivariate Models Employing Monthly Data, Tourism Management, 46, pp. 123-135.
• Hazır, Ender; Koç, Küçük Hüseyin; Esnaf, Şakir (2015). April. Estimation of Turkey’ s Furniture Sales Values with An Example of Artificial Intelligence Application., Proceedings of 3rd. National Furniture Congress (UMK-2015), Konya.
• Ho, S.L.; Xie, M.; Goh, Thong N (2002). A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction, Computers&Industrial Engineering, 42, pp. 371-375.
• Hong, Wei-Chiang Samuelson; Dong, Yucheng; Chen, Li-Y.ueh; Wei, Shih-Yung (2011). SVR with Hybrid Chaotic Genetic Algorithms for Tourism Demand Forecasting, Applied Soft Computing,11, pp. 1881-1890.
• HSBC Global Research (2011). The World in 2050.
• Huang, Ya-Ling; Lee, Yen-Hsien (2011). Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand, Scientific Research, 2, pp. 823-829.
• Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Moutinho, Luiz; Wang, Yu-Chun (2012). Forecasting Tourism Demand By Fuzzy Time Series Models, International Journal of Culture, Tourism and Hospitality Research, 6(4), pp. 377-388.
• Karagöz, Kadir (2008). Turkey’ s Tourism Potential: The Gravity Model Approach, Anatolia: Journal of Tourism Research, 19 (2), pp. 149-156.
• Kaya, A.Ayşen; Canlı, Berna (2011). The Determinants of International Tourism Demand for Turkey: Panel Data Approach, Journal of Anadolu University Social Sciences, 13 (1), pp. 43-54.
• Köse, Nezir; Yalçın, Yeliz; Emirmahmutoğlu, Furkan (2008). Demand Analysis of Turkey Tourism Sector, Economics, Management and Finance, 23 (263), pp. 24-40.
• Law, Rob; Au, Norman (1999). A Neural Network Model to Forecast Japanese Demand for Travel to Hong Kong, Tourism Management, 20, pp. 89-97.
• Li, Xin; Pan, Bing; Law, Rob; Huang, Xueli (2017). Forecasting Tourism Demand with Composite Search Index, Tourism Management, 59, pp. 57-66.
• Lyu, Meng-Ning; Yang, Qing-Shan; Yang, Na; Law, Siu-Seong (2016). Tourist number prediction of historic buildings by singular spectrum analysis, Journal of Applied Statistics, 43 (5), pp. 827-846.
• Mark, Jonathan; Goldberg, Michael A. (2001). Multiple Regression Analysis and Mass Assessment: A Review of the Issues, The Appraisal Journal, pp. 89-109. Önder, A. Özlem; Candemir, Aykan; Kumral, Neşe (2009). An Empirical Analysis of the Determinants of International Tourism Demand: The Case of İzmir, European Planning Studies, 17 (10), pp. 1525-1533.
• Pai, Ping-Feng; Hung, Kuo-Chen; Lin, Kuo-Ping (2014). Tourism Demand Forecasting Using Novel Hybrid System, Expert Systems with Applications, 41, pp. 3691-3702.
• Paksoy, Perihan; Çolakoğlu, Nurdan (2010). The attitude of the tourism and travel industry during periods of economic crisis, Paper presented at International Conference on Eurasian Economies.
• Pan, Bing; Wu, Doris Chenguang; Song, Haiyan (2012). Forecasting Hotel Room Demand Using Search Engine Data, Journal of Hospitality and Tourism Technology, 3 (3), pp. 196-210.
• Peng, Bo; Song, Haiyan; Crouch Geoffrey I. (2014). A Meta – Analysis of International Tourism Demand Forecasting and Implications for Practice, Tourism Management, 45, pp. 181-193.
• Reinhart, Carmen M.; Rogoff, Kenneth S. (2008). Is the 2007 US Sub-Prime Financial Crisis So Different? An International Historical Comparison, American Economic Review: Papers&Proceedings, 98(2), pp. 339-344.
• Shahrabi, Jamal; Hadavandi, Esmaiel; Asadi, Shahrokh (2013). Developing A Hybrid Intelligent Model for Forecasting Problems: Case Study of Tourism Demand Time Series, Knowledge-Based Systems, 43, pp. 112-122.
• Slutzky, Eugen (Trans.). (1927). The summation of random causes as the source of cyclic processes, Econometrica, 5, pp. 105-146.
• Song, Haiyan; Li, Gang (2008). Tourism Demand Modelling and Forecasting – A Review of Recent Research, Tourism Management, 29, pp. 203-220.
• Song, Haiyan; Gao, B. Zixuan; Lin, (Vera) Shanshan (2013). Combining Statistical and Judgemental Forecasts via a web-based Tourism Demand Forecasting System, International Journal of Forecasting, 29, pp. 295-310.
• Song, Haiyan; Lin, (Vera) Shanshan; Witt, Stephan F.; Zhang, Xinyan (2011). Impact of Financial/Economic Crisis on Demand for Hotel Rooms in Hong Kong, Tourism Management, 32, pp. 172-186.
• Soysal, Mehmet; Ömürgönülşen, Mine (2010). An Application on Demand Forecasting in Turkish Tourism Sector, Anatolia: Journal of Tourism Researches, 21 (1), pp. 128-136.
• The Statistical Office of the European Commission (2018). EUROSTAT Database.
• Tsaur, Ruey- Chyn; Kuo, Ting – Chun (2011). The Adaptive Fuzzy Time Series Model with an Application to Taiwan’ s tourism demand. Expert Systems with Applications, 38, pp. 9164-9171.
• Turkish Rebuplic Development Ministry International Economic Indicators (2014).
• Türkben, Cihat; Gül, Fulya; Uzar, Yılmaz (2012). The role and importance of agricultural tourism (argo-tourism) in viticulture in Turkey, KMÜ Social and Economic Research Journal, 14 (23), pp. 47-50.
• Wang, Yu-Shan (2009). The Impact of Crisis Events and Macroeconomic Activity on Taiwan’ s International Inbound Tourism Demand, Tourism Management, 30, pp. 75-82.
• Wei, William W. S. (2006). Time series analysis univariate and multivariate methods, 2nd ed., Boston, San Franciaco, New York.
• Wei, Yu; Chen, Mu-Chen (2012). Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks, Transportation Research, Part C, 21, pp. 148-162.
• Williams, Billy M.; Durvasula, Priya K.; Brown, Donald E. (1998). Urban Freeway Traffic Flow Prediction Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models, Transportation Research Recard, Paper No. 98-0463.
• World tourism organization (UNWTO), April (2013).
• Wu, Qi; Law, Rob; Xu, Xin (2012). A Sparse Gaussian Process Regression Model for Tourism Demand Forecasting in Hong Kong, Expert Systems With Applications, 39, pp. 4769-4774.
• Zhang, G. Peter (2003). Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, pp. 159-175.
• Zhang, Guoqiang; Patuwo, Eddy B.; Hu, Michael Y. (1998). Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14, pp. 35-62.
• www.aa.com.tr