Repository logo
 
Publication

Analyzing and forecasting tourism demand in Vietnam with artificial neural networks

2021Journal article Open access
Simple item page
dc.contributor.authorNguyen, Le Quyen
dc.contributor.authorFernandes, Paula Odete
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2022-05-16T13:54:32Z
dc.date.available2022-05-16T13:54:32Z
dc.date.issued2021
dc.description.abstractVietnam has experienced a tourism expansion over the last decade, proving itself as one of the top tourist destinations in Southeast Asia. The country received more than 18 million international tourists in 2019, compared to only 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and incomes for Vietnam’s tourism sector, making it the key driver to the socio-economic development of the country. Following the COVID-19 pandemic, only 3.8 million international tourists visited Vietnam in 2020, plummeting by 78.7% year-on-year. The latest outbreak in early summer 2021 made the sector continue to hit bottom. Although Vietnam’s tourism has suffered extreme losses, once the contagion is under control worldwide, the number of international tourists to Vietnam is expected to rise again to reach pre-pandemic levels in the next few years. First, the paper aims to provide a summary of Vietnam’s tourism characteristics with a special focus on international tourists. Next, the predictive capability of artificial neural network (ANN) methodology is examined with the datasets of international tourists to Vietnam from 2008 to 2020. Some ANN architectures are experimented with to predict the monthly number of international tourists to the country, including some lockdown periods due to the COVID-19 pandemic. The results show that, with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can be forecast for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam’s policymakers and firm managers to make better investment and strategic decisions.pt_PT
dc.description.sponsorshipThis work was supported by National Funds through the Fundação para a Ciência e Tecnologia (FCT) under the projects UIDB/GES/04752/2020 and UIDB/05757/2020. The research received also financial support under the project “BIOMA—Bioeconomy integrated solutions for the mobilization of the Agro-food market” (POCI-01-0247-FEDER-046112), by “BIOMA” Consortium, and financed by European Regional Development Fund (FEDER).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNguyen, Le Quyen; Fernandes, Paula O.; Teixeira, João Paulo (2021). Analyzing and forecasting tourism demand in Vietnam with artificial neural networks. Forecasting. ISSN 2571-9394. 4:1, p. 1-15pt_PT
dc.identifier.doi10.3390/forecast4010003pt_PT
dc.identifier.issn2571-9394
dc.identifier.urihttp://hdl.handle.net/10198/25451
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationUIDB/GES/04752/2020pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectTourism support decision systempt_PT
dc.subjectForecasting with ANNpt_PT
dc.subjectTourism forecasting in COVID-19 pandemic periodpt_PT
dc.subjectVietnam’s tourism demandpt_PT
dc.titleAnalyzing and forecasting tourism demand in Vietnam with artificial neural networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage50pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage36pt_PT
oaire.citation.titleForecastingpt_PT
oaire.citation.volume4pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameNguyen
person.familyNameFernandes
person.familyNameTeixeira
person.givenNameLe Quyen
person.givenNamePaula Odete
person.givenNameJoão Paulo
person.identifier1979841
person.identifierN-3804-2013
person.identifier663194
person.identifier.ciencia-id831F-3D93-525D
person.identifier.ciencia-id991D-9D1E-D67D
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-4040-2018
person.identifier.orcid0000-0001-8714-4901
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id35200741800
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication67246437-fbc9-44b2-bb97-deb53811ec44
relation.isAuthorOfPublication2269147c-2b53-4d1c-bc1b-f1367d197262
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
forecasting-04-00003-v2.pdf
Size:
1.78 MB
Format:
Adobe Portable Document Format
Download

Collections

ESTiG - Artigos em Revistas Indexados à WoS/Scopus