A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Turkiye

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dc.authoridTANRIKULU, Ceyda/0000-0001-9025-583X
dc.authoridTutsoy, Onder/0000-0001-6385-3025
dc.contributor.authorTutsoy, Önder
dc.contributor.authorTanrikulu, Ceyda
dc.date.accessioned2025-01-06T17:43:33Z
dc.date.available2025-01-06T17:43:33Z
dc.date.issued2022
dc.description.abstractThe most important underlying reasons for marketing failures are incomplete understanding of customer wants and needs and the inability to accurately predict their future behaviors. This study develops a machine learning model to estimate the number of departing foreign visitors from Turkiye by reasons for the next 10 years to gain a deeper understanding of their future behaviors. The data between 2003 and 2021 are extensively analyzed, and a multi-dimensional model having a higher-order fractional-order polynomial structure is constructed. The resulting model can predict the 10 reasons of departing foreign visitors for the next 10 years and can update the predictions every year as new data becomes available as it has stable polynomial parameters. In addition, a batch-type genetic algorithm is modified to learn the unknown model parameters by considering the disruptions, such as the coup attempt in 2016 and the COVID-19 pandemic outbreak in 2019, termed as uncertainties. Thus, the model can estimate the overall behavior of the departing foreign visitors in the presence of uncertainties, which is the dominant character of the foreign visitors by their reasons. Furthermore, the developed model is utterly data-driven, meaning it can be trained with the data collected from different cities, regions, and countries. It is predicted that the departing foreign visitors for all reasons will increase at various rates between 2022 and 2031, while the increase in transit visitors is predicted to be higher than the others. The results are discussed, and suggestions are given considering the marketing science. This study can be helpful for global and local firms in tourism, governmental agencies, and civil society organizations.
dc.identifier.doi10.3390/app122111163
dc.identifier.issn2076-3417
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85141847366
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app122111163
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2706
dc.identifier.volume12
dc.identifier.wosWOS:000883492300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectmarketing
dc.subjecttourism
dc.subjectTurkiye
dc.subjectmachine learning
dc.subjectfractional-order polynomial prediction
dc.titleA Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Turkiye
dc.typeArticle

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