Time Series Forecasting Of Tourist Overnights In Slovakia, Hungary, And The Czech Republic: Implications For Economic Performance

Abstract

Tourism is a key economic sector with a positive impact on national economic development, including in countries without direct access to coastal tourism. This study focuses on time series forecasting of tourist overnight stays in Slovakia, Hungary, and the Czech Republic for the period 2001–2025, based on Eurostat monthly data. Using ARIMA-based models, the analysis identifies seasonal and long-term trends in tourism demand and produces short-term forecasts for each country. The selection of overnight stays as the main indicator reflects its strong correlation with tourist spending and broader economic impact. In addition to forecasting, the study explores potential links potential links between tourism development and national economic performance, highlighting tourist overnight stays as a proxy indicator of the tourism sector’s contribution to the economy in these Central European countries. By combining statistical modeling with economic interpretation, the study supports informed policymaking and strategic planning in the post-pandemic tourism landscape. The integration of AI-based approaches is suggested as a promising avenue for enhancing future forecasting accuracy and capturing complex patterns.