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Using annual data from 2010 to 2020 for 40 countries, the authors start with 34 variables, then apply data prefiltering to obtain 17 relevant variables for 24 countries. To examine uncertainty effects on international tourism, they incorporate uncertainty measures including the World Pandemic Uncertainty (WPU) Index, Global CBOE Volatility Index, Political and Economic Globalisation indices, and the Political Stability Index. The study compares six machine learning algorithms and finds Gradient-Boosting Trees as best, achieving the most accurate forecasts with MAPE 0.10% (training) and 1.36% (out-of-sample).",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/tourism-and-uncertainty-a-machine-learning-approach-research-article/290188/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/tourism-and-uncertainty-a-machine-learning-approach-research-article/290188.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What forecasting task does the paper address?","Question",{"text":113,"@type":114},"It develops a model to forecast out-of-sample tourism demand in 24 European Union countries.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the study operationalize uncertainty in tourism?",{"text":118,"@type":114},"It adds multiple uncertainty-related variables, including the WPU Index, CBOE Volatility Index, Political and Economic Globalisation indices, and the Political Stability Index.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning approach performs best and what is the reported accuracy?",{"text":122,"@type":114},"Gradient-Boosting Trees outperforms the other models, with MAPE of 0.10% in training and 1.36% in out-of-sample tests.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290188,1789640899,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":26,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","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 | [https://doi.org/10.1080/13683500.2024.2370380](https://doi.org/10.1080/13683500.2024.2370380) |\n| Date | 2024 |\n| Citation | Dimitriadou, Athanasia, Gogas, Periklis and Papadimitriou, Theophilos (2024) Tourism and uncertainty: a machine learning approach. Current Issues in Tourism. ISSN 1368-3500 |\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. [https://doi.org/10.1080/13683500.2024.2370380](https://doi.org/10.1080/13683500.2024.2370380)  \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/)  \nCentral 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 ","cbCaibLugKJ3cG5E","https://ap.wps.com/l/cbCaibLugKJ3cG5E","pdf",1439585,24,"English","# Abstract\n# Introduction\n## Research design and data preparation\n## Uncertainty measures\n## Machine learning models and comparison\n## Results and findings","[{\"question\":\"What forecasting task does the paper address?\",\"answer\":\"It develops a model to forecast out-of-sample tourism demand in 24 European Union countries.\"},{\"question\":\"How does the study operationalize uncertainty in tourism?\",\"answer\":\"It adds multiple uncertainty-related variables, including the WPU Index, CBOE Volatility Index, Political and Economic Globalisation indices, and the Political Stability Index.\"},{\"question\":\"Which machine learning approach performs best and what is the reported accuracy?\",\"answer\":\"Gradient-Boosting Trees outperforms the other models, with MAPE of 0.10% in training and 1.36% in out-of-sample tests.\"}]","Tourism and uncertainty: a machine learning approach - Research article | PDF"]