Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks

Le Quyen Nguyen, Paula Odete Fernandes and João Paulo Teixeira
Additional contact information
Le Quyen Nguyen: UNIAG-Applied Management Research Unit, Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal
Paula Odete Fernandes: UNIAG-Applied Management Research Unit, Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal
João Paulo Teixeira: UNIAG-Applied Management Research Unit, Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal

Forecasting, 2021, vol. 4, issue 1, 1-15

Abstract: Vietnam 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.

Keywords: tourism support decision system; forecasting with ANN; tourism forecasting in COVID-19 pandemic period; Vietnam’s tourism demand (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-9394/4/1/3/pdf (application/pdf)
https://www.mdpi.com/2571-9394/4/1/3/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:4:y:2021:i:1:p:3-50:d:712898

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().