A big data analytics method for forecasting tourism flows.

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dc.contributor.advisor Casarin, Roberto it_IT
dc.contributor.author Gaifeev, Bulat <1995> it_IT
dc.date.accessioned 2019-06-19 it_IT
dc.date.accessioned 2019-11-20T07:10:45Z
dc.date.issued 2019-07-16 it_IT
dc.identifier.uri http://hdl.handle.net/10579/15547
dc.description.abstract This paper was motivated by the fact that there is an increasing share of government and commercial organizations’ profit from tourists in European countries. The model used to analyze tourist flows considers a city as a dynamic flow network where the nodes of it are represented by sightseeing and other points of attractions including hotels, shops, etc. Dynamic generalized linear model specification of Bayesian multivariate time-series analysis methodology was implemented, where the tourist flows are conditional Poisson distributed observable variables which are expected to be determined by latent parameter changing over time, so there is a hidden Markov chain. More sophisticated and evolved versions of the model allowing to investigate seasonal trends present in the tourism industry were estimated as well. A computational part of the case-study is done using the R programming language. A possibility to make real-time predictions within specific confidence interval allowing to approximate demand level and to increase the efficiency of services is an example of practical benefits of the work. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Bulat Gaifeev, 2019 it_IT
dc.title A big data analytics method for forecasting tourism flows. it_IT
dc.title.alternative A big data analytics method for forecasting tourist flows it_IT
dc.type Master's Degree Thesis it_IT
dc.degree.name Economia e finanza it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Economia it_IT
dc.description.academicyear 2018/2019_sessione_estiva it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 872057 it_IT
dc.subject.miur SECS-P/05 ECONOMETRIA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Bulat Gaifeev (872057@stud.unive.it), 2019-06-19 it_IT
dc.provenance.plagiarycheck Roberto Casarin (r.casarin@unive.it), 2019-07-08 it_IT

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