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dc.contributor.author
Saltsidou, Eleni
en
dc.date.accessioned
2021-09-29T11:46:45Z
dc.date.available
2021-09-29T11:46:45Z
dc.date.issued
2021-09-29
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29915
dc.rights
Default License
dc.subject
Tourism demand
en
dc.title
Predicting tourism demand in Greece using time-series
en
heal.type
masterThesis
en_US
heal.dateAvailable
2021-01-18
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2021-01-22
heal.abstract
Tourist arrivals are an essential factor to understand the tourism demand industry and its trends. Accurate forecasts of tourism demand are necessary for stakeholders so they can organize an efficient plan to properly distribute their resources. Tourism demand forecasting is attracting attention the recent years. Many methods are synthesized in order to produce an optimal model that has an accurate forecasting performance. This research presents several time-series models to forecast the tourism demand and more specifically the tourist arrivals to a destination. After the pre-processing of the data of tourist arrivals to Ionian islands and Western Macedonia from 2010 to 2018, several time-series models are implemented to produce 24-month and 12-month forecasts. The generated forecasts are assessed in order to measure the accuracy of the models. The research showed that although the SARIMA model did very well in the forecasts, there was no optimal model for all the cases that were tested.
en
heal.advisorName
Drakaki, Maria
en
heal.committeeMemberName
Tsirigotis, Georgios
en
heal.committeeMemberName
Baltatzis, Dimitrios
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US
heal.spatialCoverage
Greece
en


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