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

İhsan Erdem Kayral (), Tuğba Sarı and Nisa Şansel Tandoğan Aktepe
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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

Sustainability, 2023, vol. 15, issue 22, 1-20

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.

Keywords: tourism income forecasting; tourist arrival forecasting; COVID-19; artificial neural networks; grey forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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