Forecasting tourism demand for Lisbon’s region through a data mining approach

Bibliographic Details
Main Author: Ricardo, Hugo
Publication Date: 2018
Other Authors: Gonçalves, Ivo, Costa, Ana Cristina
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/145899
Summary: Ricardo, H., Ivo, G., & Costa, A. C. (2018). Forecasting tourism demand for Lisbon’s region through a data mining approach. In M. B. Nunes, P. Isaías, & P. Powell (Eds.), Proceedings of the 11th IADIS International Conference Information Systems 2018 (pp. 58-66). IADIS Press. ISBN: 978-989-8533-74-6
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spelling Forecasting tourism demand for Lisbon’s region through a data mining approachForecastKnowledge discoveryLisbonMachine learningTourismHardware and ArchitectureSoftwareComputer Science ApplicationsInformation SystemsSDG 11 - Sustainable Cities and CommunitiesRicardo, H., Ivo, G., & Costa, A. C. (2018). Forecasting tourism demand for Lisbon’s region through a data mining approach. In M. B. Nunes, P. Isaías, & P. Powell (Eds.), Proceedings of the 11th IADIS International Conference Information Systems 2018 (pp. 58-66). IADIS Press. ISBN: 978-989-8533-74-6Tourism stakeholders such as the government, passenger transport companies, accommodation establishments, restaurants, recreational businesses, among others, rely on tourism demand indicators’ forecasts to make decisions. Most of tourism demand forecasting models are time-series and econometric based. Machine learning methods are emerging and have been proved to be quite suitable for non-linear modelling. These methods are part of an interdisciplinary field named “Data Mining” which is known by the process of knowledge discovery in databases (KDD). The core drive of this work is to enhance the available public sources of tourism forecast information and to contribute to the tourism stakeholders’ strategy in Portugal. More specifically, a multivariate model to forecast international tourism demand was developed through a Data Mining approach, which assessed models derived by Regression Trees (Random Forests), Artificial Neural Networks and, Support Vector Machines (SVM). The model development was constrained to machine learning methods, publicly available data, and minimum data assumptions. The forecasted demand variable was the nights spent at tourist accommodation establishments in Lisbon’s region, one of the country’s main foreign tourist destinations. The objectives were achieved, as the selected model (SMOReg, support vector regression) was successful in generalization capability. The accuracy of the produced forecasts provides some evidence of the reliability of the proposed model. If institutions and decision makers have information regarding the evolution of the explanatory variables used in this model, the impact on Lisbon’s tourism demand can be assessed, even in case of an emerging recession, as shown using three future plausible scenarios.IADIS PressNOVA Information Management School (NOVA IMS)RUNRicardo, HugoGonçalves, IvoCosta, Ana Cristina2022-11-29T22:14:52Z2018-01-012018-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion9application/pdfhttp://hdl.handle.net/10362/145899eng9789898533746PURE: 12363332info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:07:03Zoai:run.unl.pt:10362/145899Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:37:26.383152Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting tourism demand for Lisbon’s region through a data mining approach
title Forecasting tourism demand for Lisbon’s region through a data mining approach
spellingShingle Forecasting tourism demand for Lisbon’s region through a data mining approach
Ricardo, Hugo
Forecast
Knowledge discovery
Lisbon
Machine learning
Tourism
Hardware and Architecture
Software
Computer Science Applications
Information Systems
SDG 11 - Sustainable Cities and Communities
title_short Forecasting tourism demand for Lisbon’s region through a data mining approach
title_full Forecasting tourism demand for Lisbon’s region through a data mining approach
title_fullStr Forecasting tourism demand for Lisbon’s region through a data mining approach
title_full_unstemmed Forecasting tourism demand for Lisbon’s region through a data mining approach
title_sort Forecasting tourism demand for Lisbon’s region through a data mining approach
author Ricardo, Hugo
author_facet Ricardo, Hugo
Gonçalves, Ivo
Costa, Ana Cristina
author_role author
author2 Gonçalves, Ivo
Costa, Ana Cristina
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Ricardo, Hugo
Gonçalves, Ivo
Costa, Ana Cristina
dc.subject.por.fl_str_mv Forecast
Knowledge discovery
Lisbon
Machine learning
Tourism
Hardware and Architecture
Software
Computer Science Applications
Information Systems
SDG 11 - Sustainable Cities and Communities
topic Forecast
Knowledge discovery
Lisbon
Machine learning
Tourism
Hardware and Architecture
Software
Computer Science Applications
Information Systems
SDG 11 - Sustainable Cities and Communities
description Ricardo, H., Ivo, G., & Costa, A. C. (2018). Forecasting tourism demand for Lisbon’s region through a data mining approach. In M. B. Nunes, P. Isaías, & P. Powell (Eds.), Proceedings of the 11th IADIS International Conference Information Systems 2018 (pp. 58-66). IADIS Press. ISBN: 978-989-8533-74-6
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2018-01-01T00:00:00Z
2022-11-29T22:14:52Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145899
url http://hdl.handle.net/10362/145899
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9789898533746
PURE: 12363332
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
application/pdf
dc.publisher.none.fl_str_mv IADIS Press
publisher.none.fl_str_mv IADIS Press
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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