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Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Author

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  • İhsan Erdem Kayral

    (Department of International Trade and Finance, Faculty of Economic and Administrative Sciences, Ostim Technical University, Ankara 06374, Türkiye)

  • Tuğba Sarı

    (Department of Management Information Systems, Faculty of Social Sciences and Humanities, Konya Food and Agriculture University, Konya 42080, Türkiye)

  • Nisa Şansel Tandoğan Aktepe

    (Department of Economics, Faculty of Economic and Administrative Sciences, Hacettepe University, Ankara 06800, Türkiye)

Abstract

Accurate forecasting of tourism demand and income holds paramount importance for both the tourism industry and the national economy. This study aims to address several objectives: (1) specify the best forecasting model in the prediction of tourist arrival volumes and tourism income for Turkey; (2) assess the degree of impact exerted by various determinants on the tourism forecasts; (3) generate forecasts for tourist arrival volumes and tourism income using the most suitable models; and (4) examine potential scenarios illustrating the ramifications of the Russia-Ukraine war on tourist arrival volumes and tourism income. The forecasting models employed in this study encompass a comprehensive set of statistical methods, including ETS, ARIMA, TRAMO-SEATS, X13, X11, STL, Grey, and their combinations with ANN. In the ANN models, exogenous variables such as the global financial crisis, the Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates are incorporated. The results unveil the identification of five superior models: ETS, Grey, hybrid ETS-ANN, hybrid Grey-ANN, and hybrid ARIMA-ANN models, which exhibit the lowest MAPE and sMAPE values. Forecasts for the forthcoming quarters are examined under two scenarios: assuming the continuity or cessation of the Russia-Ukraine war. Comparative analysis of the relative effects of exogenous variables indicates that COVID-19 has the most substantial impact on tourist arrival volumes, and tourism income is primarily influenced by the USD/TRY exchange rate.

Suggested Citation

  • İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15924-:d:1279810
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    References listed on IDEAS

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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    3. Wu, Lifeng & Liu, Sifeng & Fang, Zhigeng & Xu, Haiyan, 2015. "Properties of the GM(1,1) with fractional order accumulation," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 287-293.
    4. Maria M. De Mello & Natércia Fortuna, 2005. "Testing Alternative Dynamic Systems for Modelling Tourism Demand," CEF.UP Working Papers 0501, Universidade do Porto, Faculdade de Economia do Porto.
    5. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    6. Biljana Petrevska, 2017. "Predicting tourism demand by A.R.I.M.A. models," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 30(1), pages 939-950, January.
    7. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2013. "“Tourism demand forecasting with different neural networks models”," IREA Working Papers 201321, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
    9. Maria M. De Mello & Natércia Fortuna, 2005. "Testing Alternative Dynamic Systems for Modelling Tourism Demand," Tourism Economics, , vol. 11(4), pages 517-537, December.
    10. Kaijian He & Don Wu & Yingchao Zou, 2022. "Tourist Arrival Forecasting Using Multiscale Mode Learning Model," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

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