Bagging in Tourism Demand Modeling and Forecasting | Zendy

George Athanasopoulos | Zendy; Haiyan Song | Zendy; Jonathan A. Sun | Zendy

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Bagging in Tourism Demand Modeling and Forecasting

Author(s) -

George Athanasopoulos,

Haiyan Song,

Jonathan A. Sun

Publication year - 2017

Publication title -

journal of travel research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.403

H-Index - 132

eISSN - 1552-6763

pISSN - 0047-2875

DOI - 10.1177/0047287516682871

Subject(s) - tourism , econometrics , computer science , demand forecasting , model selection , feature selection , variable (mathematics) , inference , selection (genetic algorithm) , statistical inference , predictive modelling , machine learning , artificial intelligence , economics , operations research , statistics , engineering , mathematics , geography , mathematical analysis , archaeology

This study introduces bootstrap aggregation (bagging) in modeling and forecasting tourism demand. The aim is to improve the forecast accuracy of predictive regressions while considering fully automated variable selection processes which are particularly useful in industry applications. The procedures considered for variable selection is the general-to-specific (GETS) approach based on statistical inference and stepwise search procedures based on a measure of predictive accuracy (MPA). The evidence based on tourist arrivals from six source markets to Australia overwhelmingly suggests that bagging is effective for improving the forecasting accuracy of the models considered.School of Hotel and Tourism Management2016-2017 > Academic research: refereed > Publication in refereed journalbcm

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