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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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| Published in: | Sustainability 2022-02, Vol.14 (4), p.1964 |
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| Main Authors: | , , , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c295t-dfab8361427448fc3ac03a02daf9291187d6d9422829b5954177e43d0a8734263 |
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| container_issue | 4 |
| container_start_page | 1964 |
| container_title | Sustainability |
| container_volume | 14 |
| creator | Turtureanu, Anca-Gabriela Pripoaie, Rodica Cretu, Carmen-Mihaela Sirbu, Carmen-Gabriela Marinescu, Emanuel Ştefan Talaghir, Laurentiu-Gabriel Chițu, Florentina |
| description | 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). |
| doi_str_mv | 10.3390/su14041964 |
| format | article |
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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).</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14041964</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Arrivals ; Consumption ; Coronaviruses ; COVID-19 ; Decision making ; Demand analysis ; 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</subject><ispartof>Sustainability, 2022-02, Vol.14 (4), p.1964</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| identifier | ISSN: 2071-1050 |
| ispartof | Sustainability, 2022-02, Vol.14 (4), p.1964 |
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| language | eng |
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| source | Publicly Available Content (ProQuest); Coronavirus Research Database |
| subjects | Arrivals Consumption Coronaviruses COVID-19 Decision making Demand analysis 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 |
| title | A Projection Approach of Tourist Circulation under Conditions of Uncertainty |
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