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Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks

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  • 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
    CEDRI-Research Center in Digitalization and Intelligent Robotics, Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal)

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.

Suggested Citation

  • Le Quyen Nguyen & Paula Odete Fernandes & João Paulo Teixeira, 2021. "Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks," Forecasting, MDPI, vol. 4(1), pages 1-15, December.
    • Handle: RePEc:gam:jforec:v:4:y:2021:i:1:p:3-50:d:712898
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    References listed on IDEAS

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    1. Kourentzes, Nikolaos & Athanasopoulos, George, 2019. "Cross-temporal coherent forecasts for Australian tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 393-409.
    2. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    3. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
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    Cited by:

    1. El houssin Ouassou & Hafsa Taya, 2022. "Forecasting Regional Tourism Demand in Morocco from Traditional and AI-Based Methods to Ensemble Modeling," Forecasting, MDPI, vol. 4(2), pages 1-18, April.

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