A review of research on tourism demand forecasting

被引:302
|
作者
Song, Haiyan [1 ]
Qiu, Richard T. R. [1 ]
Park, Jinah [1 ]
机构
[1] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Hong Kong, Peoples R China
关键词
Tourism demand; Time series; Econometric model; Forecast combination; Artificial intelligence model; Judgment forecasts; SUPPORT VECTOR REGRESSION; TIME-VARYING PARAMETER; NEURAL-NETWORK MODEL; INTERNATIONAL TOURISM; ECONOMIC-CRISIS; COINTEGRATION ANALYSIS; INBOUND TOURISM; UNITED-STATES; TRAVEL DEMAND; ECONOMETRIC FORECASTS;
D O I
10.1016/j.annals.2018.12.001
中图分类号
F [经济];
学科分类号
02 ;
摘要
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, a special selection of research in this field.
引用
收藏
页码:338 / 362
页数:25
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