Bagging in tourism demand modeling and forecasting

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/72201

Title:	Bagging in tourism demand modeling and forecasting
Authors:	Athanasopoulos, G Song, H Sun, JA
Issue Date:	1-Jan-2018
Source:	Journal of travel research, 1 Jan. 2018, p. 52-68
Abstract:	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.
Keywords:	Australia Bootstrap aggregation Model selection Predictive regression
Publisher:	SAGE Publications
Journal:	Journal of travel research
ISSN:	0047-2875
EISSN:	1552-6763
DOI:	10.1177/0047287516682871
Rights:	This is the accepted version of the publication Athanasopoulos, G., Song, H., & Sun, J. A., Bagging in tourism demand modeling and forecasting, Journal of Travel Research (Volume 57 and issue 1) pp. 52-68. Copyright © 2017 (The Author(s)). DOI: 10.1177/0047287516682871.
Appears in Collections:	Journal/Magazine Article

File	Description	Size	Format
14._Bagging_JTR.pdf	Pre-Published version	1.61 MB	Adobe PDF	View/Open

Status	open access
File Version	Final Accepted Manuscript

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