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Predictivity of tourism demand data

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dc.contributor.authorYishuo, Zhang
dc.contributor.authorGang, Li
dc.contributor.authorMuskat, Birgit
dc.contributor.authorVu, Quan Huy
dc.contributor.authorLaw, Rob
dc.date.accessioned2021-11-05T03:40:28Z
dc.date.issued2021-07
dc.description.abstractAs tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock — such as SARS over tourism demand forecasting.en_AU
dc.description.sponsorshipThe work was completed when Gang Li was on ASL in Chinese Academy of Sciences in 2019, and we would like to thank Deakin University's ASL2019 fund and Xinjiang Science & Technology Research fund with Chinese Academy of Sciences.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0160-7383
dc.identifier.urihttp://hdl.handle.net/1885/251610
dc.language.isoen_AUen_AU
dc.provenancehttps://v2.sherpa.ac.uk/id/publication/10583..."The Accepted Version can be archived in an Institutional Repository 36 Months after publication with CC BY-NC-ND." from SHERPA/RoMEO site (as at 5/11/2021).en_AU
dc.publisherElsevieren_AU
dc.rights© 2021 Elsevier Ltden_AU
dc.rights.licenseCC BY-NC-NDen_AU
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_AU
dc.sourceAnnals of Tourism Researchen_AU
dc.subjectData characteristicsen_AU
dc.subjectEntropyen_AU
dc.subjectPredictivityen_AU
dc.subjectTourism demand forecastingen_AU
dc.titlePredictivity of tourism demand dataen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Access after embargo endsen_AU
local.bibliographicCitation.lastpage103234-16en_AU
local.bibliographicCitation.startpage103234-1en_AU
local.contributor.affiliationBirgit Muskat, Research School of Management, ANU College of Business & Economics, The Australian National Universityen_AU
local.contributor.authoruidu1095759en_AU
local.description.embargo2024-07-30
local.identifier.ariespublicationa383154xPUB19691
local.identifier.ariespublicationu4868915xPUB264
local.identifier.citationvolume89en_AU
local.publisher.urlhttps://www.elsevier.com/en-auen_AU
local.type.statusAccepted Versionen_AU

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