Goal: The tourism sector always plays a vital role among other sectors, which contributes heavily to Thailand’s GDP. Due to the recent Pandemic crises, the tourism industry worldwide faces a decline. The aim of writing this paper is to forecast, tourist inflow for Thailand for the next nine months by using ARIMA forecasting Design / Methodology / Approach: In order to meet the study objective, we adopted the univariate time series forecasting of tourist arrival. The AR(12) and MA(12) process with d(1) order to specify the appropriate model also applied for forecasting selection of international tourist arrivals, after diagnostic checking of series through correlogram Q-statistics and correlogram of residuals for adjusted ARIMA (12,1,12) in which we found low residual against 70 lags. Results: The evidence predicts that in the next nine months, Thailand will face significant negative zone arrival of international tourists due to the COVID-19 pandemic crises, which adversely affect Thailand’s economy due to the shortfall of international tourist arrivals. However decline in tourist arrival will eventually decrease air pollution in Thailand, thus it positively impact on environmental quality. Limitations of the investigation: In terms of limitation of this study, this forecasting is only valid for Thailand based on historical and current data. Practical implications: We highly recommend government of Thailand for enchasing overall logistics performance and furthermore increase use to renewable energy and green performance in their tourism sector. Originality / Value: This study provides tourism forecasting estimation for Thailand, which can help government to reshape their tourism policies due to Covid-19 pandemic crises. As this study is the first attempt to provide forecasting related with Covid-19 for tourism sector. © 2021, Associacao Brasileira de Engenharia de Producao. All rights reserved.
Impact of COVID-19 pandemic on logistics performance, economic growth and tourism industry of Thailand: An empirical forecasting using ARIMA