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A Projection Approach of Tourist Circulation under Conditions of Uncertainty

Author

Listed:
  • Anca-Gabriela Turtureanu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Rodica Pripoaie

    (Faculty of Law and Administrative Sciences, “Dunărea de Jos” University of Galati, 800008 Galati, Romania)

  • Carmen-Mihaela Cretu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Carmen-Gabriela Sirbu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Emanuel Ştefan Marinescu

    (Faculty of Communication and International Relations, Danubius University of Galati, 800654 Galati, Romania)

  • Laurentiu-Gabriel Talaghir

    (Faculty of Physical Education and Sport, Dunarea de Jos University of Galati, 800008 Galati, Romania
    Institute of Sport, Tourism and Service, South Ural State University, 454080 Chelyabinsk, Russia)

  • Florentina Chițu

    (The Economics and International Business Doctoral School, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

Abstract

This paper explores an important problem in tourism demand analysis, namely, the inherent uncertainty involved in projecting tourism demand. Tourism demand continues to be severely affected by unforeseen events associated with the current global health crisis, which has led to an examination of ways to predict the devastating effects of the COVID-19 pandemic on tourism. Tourism flow forecasting relating to arrivals is of particular importance for tourism and the entire hospitality industry, because it is an indicator of future demand. Thus, it provides fundamental information that can be applied in the planning and development of future strategies. Accurate forecasts of seasonal tourist flows can help decision-makers increase the efficiency of their strategic planning and reduce the risk of decision-making failure. Due to the growing interest in more advanced forecasting methods, we applied the ARMA model method to analyze the evolution of monthly arrival series for Romania in the period from January 2010 to September 2021, in order to ascertain the best statistical forecasting model for arrivals. We conducted this research to find the best method of forecasting tourist demand, and we compared two forecasting models: AR(1)MA(1) and AR(1)MA(2). Our study results show that the superior model for the prediction of tourist demand is AR(1)MA(1).

Suggested Citation

  • Anca-Gabriela Turtureanu & Rodica Pripoaie & Carmen-Mihaela Cretu & Carmen-Gabriela Sirbu & Emanuel Ştefan Marinescu & Laurentiu-Gabriel Talaghir & Florentina Chițu, 2022. "A Projection Approach of Tourist Circulation under Conditions of Uncertainty," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1964-:d:745338
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    Cited by:

    1. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    2. Yuzhen Li & Guofang Gong & Fengtai Zhang & Lei Gao & Yuedong Xiao & Xingyu Yang & Pengzhen Yu, 2022. "Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    3. Andrei-Florin Băbăț & Mirela Mazilu & Amalia Niță & Ionuț-Adrian Drăguleasa & Mihaela Grigore, 2023. "Tourism and Travel Competitiveness Index: From Theoretical Definition to Practical Analysis in Romania," Sustainability, MDPI, vol. 15(13), pages 1-26, June.

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