Can bagging improve the forecasting performance of tourism demand models?

Haiyan Song, Stephen F. Witt, Richard T. Qiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)419-433
Number of pages15
JournalStudies in Computational Intelligence
Publication statusPublished - 1 Feb 2017


  • Bagging
  • General-to-specific modeling
  • Hong Kong
  • Tourism demand

ASJC Scopus subject areas

  • Artificial Intelligence


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