Tourism demand forecasting: a decomposed deep learning approach Zhang, Yishuo, Li, Gang, Muskat, Birgit and Law, Rob 2020, Tourism demand forecasting: a decomposed deep learning approach, Journal of travel research, pp. 1-17, doi: 10.1177/0047287520919522. Attached Files Name Description MIMEType Size Downloads Title Tourism demand forecasting: a decomposed deep learning approach Author(s) Zhang, Yishuo Li, Gang orcid.org/0000-0003-1583-641X Muskat, Birgit Law, Rob Journal name Journal of travel research Start page 1 End page 17 Total pages 17 Publisher Sage Place of publication London, Eng. Publication date 2020 ISSN 0047-2875 1552-6763 Keyword(s) tourism demand forecasting tourism planning AI-based forecasting deep learning decomposing method overfitting Language eng DOI 10.1177/0047287520919522 Indigenous content off Field of Research 1506 Tourism 1505 Marketing 1504 Commercial Services HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30139367 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 25 Jun 2020, 14:26:36 EST Fri, 26 Jun 2020, 05:01:15 EST Fri, 26 Jun 2020, 06:06:08 EST Mon, 29 Jun 2020, 16:51:27 EST Mon, 29 Jun 2020, 16:54:52 EST Mon, 29 Jun 2020, 16:55:32 EST Sat, 04 Jul 2020, 02:31:13 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 32 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 25 Jun 2020, 14:26:36 EST

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