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Title: A review of research on tourism demand forecasting : launching the Annals of Tourism Research Curated Collection on tourism demand forecasting
Authors: Song, H 
Qiu, RTR 
Park, J 
Issue Date: Mar-2019
Source: Annals of tourism research, Mar. 2019, v. 75, p. 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.
This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.
Keywords: Artificial intelligence model
Econometric model
Forecast combination
Judgment forecasts
Time series
Tourism demand
Publisher: Pergamon Press
Journal: Annals of tourism research 
ISSN: 0160-7383
EISSN: 1873-7722
DOI: 10.1016/j.annals.2018.12.001
Rights: © 2018 Elsevier Ltd. All rights reserved.
© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The following publication Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362 is available at https://doi.org/10.1016/j.annals.2018.12.001.
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