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Title: Can bagging improve the forecasting performance of tourism demand models?
Authors: Song, H 
Witt, SF
Qiu, RT 
Issue Date: 2017
Source: Studies in computational intelligence, 2017, v. 692, p. 419-433
Abstract: This study examines the forecasting performance of the general-to-specific (GETS) models developed for Hong Kong through the bootstrap aggregating method (known as bagging). Although the literature in other research areas shows that bagging can improve the forecasting performance of GETS models, the empirical analysis in this study does not confirm this conclusion. This study is the first attempt to apply bagging to tourism forecasting, but additional effort is needed to examine the effectiveness of bagging in tourism forecasting by extending the models to cover more destination-source markets related to destinations other than Hong Kong.
Keywords: Bagging
General-to-specific modeling
Hong Kong
Tourism demand
Publisher: Springer
Journal: Studies in computational intelligence 
ISSN: 1860-949X
DOI: 10.1007/978-3-319-50742-2_25
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