[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117163-en":3,"doc-seo-117163-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117163,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Tourism and uncertainty: a machine learning approach","This paper develops an out-of-sample forecasting model for tourism demand across 24 European Union countries. The starting dataset comprises 34 annual-frequency variables covering 2010–2020 for 40 countries, reduced to 17 key variables for the final 24-country sample through data prefiltering. To examine uncertainty’s role in international tourism, the study adds multiple uncertainty measures, including the World Pandemic Uncertainty Index and volatility, political stability, and globalisation indicators. Six state-of-the-art machine learning methods are compared, and Gradient-Boosting Trees deliver the highest forecasting accuracy.","Central Lancashire Online Knowledge (CLoK)  \n\n| Title | Tourism and uncertainty: a machine learning approach |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/54916/](https://clok.uclan.ac.uk/54916/) |\n| DOI | doi10.1080/13683500.2024.2370380 |\n| Date | 2024 |\n| Citation | Dimitriadou, Athanasia, Gogas, Periklis and Papadimitriou, Theophilos (2024) Tourism and uncertainty: a machine learning approach. Current Issues in Tourism. |\n| Creators | Dimitriadou, Athanasia, Gogas, Periklis and Papadimitriou, Theophilos |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. doi10.1080/13683500.2024.2370380  \nFor information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \nCurrent Issues in Tourism  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/rcit20)[www.tandfonline.com/journals/rcit20](homepage: www.tandfonline.com/journals/rcit20)  \nTourism and uncertainty: a machine learning approach  \nAthanasia Dimitriadou, Periklis Gogas & Theophilos Papadimitriou  \nTo cite this article: Athanasia Dimitriadou, Periklis Gogas & Theophilos Papadimitriou (02 Jul 2024): Tourism and uncertainty: a machine learning approach, Current Issues in Tourism, DOI: 10. 1080/13683500 .2024.2370380  \nTo link to this article: [https://doi.org/10.1080/13683500.2024.2370380](https://doi.org/10.1080/13683500.2024.2370380)  \n Published online: 02 Jul 2024.  \n\n|  Submit your article to this journal  |\n| --- |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=rcit20](https://www.tandfonline.com/action/journalInformation?journalCode=rcit20)  \nCURRENT ISSUES IN TOURISM  \n[https://doi.org/10.1080/13683500.2024.2370380](https://doi.org/10.1080/13683500.2024.2370380)  \nRESEARCH ARTICLE   \nTourism and uncertainty: a machine learning approach  \nAthanasia Dimitriadoua, Periklis Gogasb and Theophilos Papadimitrioub  \naCollege of Business, Law and Social Sciences, University of Derby, Derby, UK; bDepartment of Economics, Democritus University of Thrace, Komotini, Greece  \nABSTRACT  \nIn this paper, we attempt to create a unique forecasting model to forecast out-of-sample the tourism demand in 24 European Union countries. The initial dataset included 34 relevant variables of annual frequency that span the period from 2010 to 2020 for 40 countries. A data prefiltering process resulted in a final set of 17 relevant variables for 24 countries. Additionally, in the effort to investigate the impact of uncertainty on international tourism, apart from the traditional factors that affect tourism, we also include variables that measure various forms of uncertainty: we use the World Pandemic Uncertainty (WPU) Index, the Global CBOE Volatility Index, the Political Globalisation Index, the Economic Globalisation Index, and the Political Stability Index. In the empirical part of our research, we employ and compare in terms of their forecasting accuracy a set of six state-of-the-art machine learning algorithms, the Support Vector Regression with both a linear and an RBF kernel, the Random Forests, the Decision Trees, the KNN, and gradient-boosting trees. The results show that the Gradient-Boosting Trees algorithm outperforms the other five models providing the most  \naccurate forecasts with a MAPE of 0.10% and 1.36% in the training and the out-of-sample tests, respectively.  \nARTICLE HIS","cbCaitxguJ7T6aKh","https://ap.wps.com/l/cbCaitxguJ7T6aKh","pdf",1307222,1,23,"English","en",105,"# Abstract\n# Introduction\n## Uncertainty and tourism demand\n## Related research on political and economic turmoil","[{\"question\":\"What is the paper trying to forecast and for which countries?\",\"answer\":\"It forecasts out-of-sample tourism demand for 24 European Union countries.\"},{\"question\":\"Which uncertainty variables are used in addition to traditional tourism factors?\",\"answer\":\"The study includes measures such as the World Pandemic Uncertainty (WPU) Index, the Global CBOE Volatility Index, the Political Globalisation Index, the Economic Globalisation Index, and the Political Stability Index.\"},{\"question\":\"Which machine learning model performed best and what accuracy was reported?\",\"answer\":\"Gradient-Boosting Trees outperformed the other models, achieving a MAPE of 0.10% in training and 1.36% in out-of-sample tests.\"}]","Tourism and uncertainty: a machine learning approach | 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