Publicação
Editorial for special issue: “Tourism forecasting: time-series analysis of world and regional data”
| dc.contributor.author | Teixeira, João Paulo | |
| dc.contributor.author | Gunter, Ulrich | |
| dc.date.accessioned | 2026-05-15T15:21:09Z | |
| dc.date.available | 2026-05-15T15:21:09Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This Special Issue was honored with six contribution papers embracing the subject of tourism forecasting. The papers focused on forecasting tourism demand in the USA, Vienna—Austria, Vietnam, Marrakech-Safi region of Morocco, Dubai, and China. The time series were spread from tourism interest in the USA, hotel room demand in Vienna, number of tourists in Vietnam, annual tourist arrivals to the Marrakech-Safi region of Morocco, tourist arrivals to Dubai from the UK and the daily and weekly number of passengers at urban rail transit stations in China. The used datasets, in some cases, included the COVID-19 pandemic period, which was a severe challenge for the forecasting models. The forecasting models used embrace the following parameters: descriptive analysis techniques, seasonal naïve, Error Trend Seasonal (ETS), Seasonal Autoregressive Integrated Moving Average (SARIMA), Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), Seasonal Neural Network Autoregression (Seasonal NNAR), Seasonal NNAR with an external regressor, Artificial Neural Network (ANN) forecasting model, ARIMA, AR, linear regression, Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) models, ensemble models, Box–Jenkins time series models, and the Facebook Prophet algorithm. The authors are in consensus in terms of concluding that the developed models serve as valuable tools for policymakers and firm managers of their countries to make better investment and strategic decisions. | por |
| dc.description.sponsorship | This research was funded by the Foundation for Science and Technology (FCT, Portugal) through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020). | |
| dc.identifier.citation | Teixeira, João Paulo; Gunter, Ulrich(2023). Editorial for special issue: “Tourism forecasting: time-series analysis of world and regional data” Forecasting. 5: 1, p. 210-212. DOI: /10.3390/forecast5010011 | |
| dc.identifier.doi | 10.3390/forecast5010011 | |
| dc.identifier.issn | 2571-9394 | |
| dc.identifier.uri | http://hdl.handle.net/10198/36693 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI AG | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation.ispartof | Forecasting | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Editorial for special issue: “Tourism forecasting: time-series analysis of world and regional data” | por |
| dc.type | editorial | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UIDB/05757/2020 | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | Forecasting | |
| oaire.citation.volume | 5 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Teixeira | |
| person.givenName | João Paulo | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 57069567500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
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