EconPapers    
Economics at your fingertips  
 

A review of research on tourism demand forecasting

Haiyan Song, Richard T.R. Qiu and Jinah Park

Annals of Tourism Research, 2019, vol. 75, issue C, 338-362

Abstract: This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.

Keywords: Tourism demand; Time series; Econometric model; Forecast combination; Artificial intelligence model; Judgment forecasts (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160738318301312
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:75:y:2019:i:c:p:338-362

Access Statistics for this article

Annals of Tourism Research is currently edited by John Tribe

More articles in Annals of Tourism Research from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-07-27
Handle: RePEc:eee:anture:v:75:y:2019:i:c:p:338-362