Editorial for Special Issue: tourism forecasting: time-series
analysis of world and regional data

Teixeira, João Paulo; Gunter, Ulrich

Publication

Editorial for Special Issue: tourism forecasting: time-series analysis of world and regional data

2023Other

dc.contributor.author	Teixeira, João Paulo
dc.contributor.author	Gunter, Ulrich
dc.date.accessioned	2023-03-21T14:35:43Z
dc.date.available	2023-03-21T14:35:43Z
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 thepandemic 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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Teixeira, João Paulo; Gunter, Ulrich (2023). Tourism forecasting: time-series analysis of world and regional data. Forecasting. 5:1, p. 210 - 212	pt_PT
dc.identifier.doi	10.3390/forecast5010011	pt_PT
dc.identifier.uri	http://hdl.handle.net/10198/27907
dc.language.iso	eng	pt_PT
dc.publisher	Forecasting	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Tourism forecasting	pt_PT
dc.subject	COVID-19	pt_PT
dc.subject	Thepandemic period	pt_PT
dc.title	Editorial for Special Issue: tourism forecasting: time-series analysis of world and regional data	pt_PT
dc.type	other
dspace.entity.type	Publication
oaire.citation.endPage	212	pt_PT
oaire.citation.issue	1	pt_PT
oaire.citation.startPage	210	pt_PT
oaire.citation.title	Forecasting	pt_PT
oaire.citation.volume	5	pt_PT
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
rcaap.rights	openAccess	pt_PT
rcaap.type	other	pt_PT
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