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