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.
Highlights
Tourism has become one of the most vibrant, robust, and fastest growing economic sectors, contributing to gross domestic product (GDP), job creation, and social and economic development along its value chain over the last decade [1]
According to the Vietnam National Administration of Tourism, in 2019, with respect to international tourists by region, short-haul markets from Asia took up the major part (79.9%), of which Northeast Asia accounted for 66.8% and Southeast Asia had a share of 11.3% [3]
According to the Vietnam National Administration of Tourism, in 2019, with respect to internationa2l00t8ou2r0i0s9ts 2b0y10re2g01io1 n2,0s1h2 or20t-1h3 au20l14ma20r1k5ets20f1r6om201A7 s2ia018too20k19up20t2h0e major part (79.9%), of which Northeast Asia accounted for 66.8% and Southeast Asia had a share of 11.3% [3]
Summary
Tourism has become one of the most vibrant, robust, and fastest growing economic sectors, contributing to gross domestic product (GDP), job creation, and social and economic development along its value chain over the last decade [1]. Forecasting tourism demand is becoming increasingly important for predicting future economic development [8]. Major groups of methods used to forecasting tourism demand include time series models, econometrics models, artificial intelligence techniques, and qualitative methods [8,36,37]. Econometric approaches, and artificial intelligence models are three main categories of quantitative forecasting methods [38]. Time series and econometrics models rely on the stability of historical patterns and economic structure, while artificial intelligence models are dependent on the quality and size of available training data [17]. Given the significance of the tourism sector to the economy, an accurate forecast of tourist demand plays an essential role in predicting the future economic development of Vietnam. Conclusions and implications for future works are presented in the final section
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