A Projection Approach of Tourist Circulation under Conditions of Uncertainty

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...

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Bibliographic Details
Published inSustainability Vol. 14; no. 4; p. 1964
Main Authors Turtureanu, Anca-Gabriela, Pripoaie, Rodica, Cretu, Carmen-Mihaela, Sirbu, Carmen-Gabriela, Marinescu, Emanuel Ştefan, Talaghir, Laurentiu-Gabriel, Chițu, Florentina
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Arrivals
Consumption
Coronaviruses
COVID-19
Decision making
Economic forecasting
Forecasting
Global health
Labor force
Literature reviews
Mathematical models
Pandemics
Public health
Risk reduction
Seasonal variations
Severe acute respiratory syndrome coronavirus 2
Social distancing
Tourism
Tourists
Uncertainty
Romania
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Summary: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).
ISSN:2071-1050
2071-1050
DOI:10.3390/su14041964