Forecasting International Tourist Arrival in Nepal by ARIMA Model

Abstract

To project Nepal's tourism growth from 2024 to 2028, use the ARIMA (1,0,0) model. It is crucial to comprehend the model's efficacy and precision in approximating both short- and long-term patterns of tourism inflows. The results show how effective the ARIMA model is, explaining 82.4% of the variations in the data, or the stationary R² = 0.824. The suitability of the model and the correlation between its residuals were confirmed by the Ljung-Box test (Statistic = 6.657) and run test (Sig. = 0.988). Due to a strong correlation between the number of visits from the previous month and the number of arrivals this month, the AR(1) parameter was highly significant (p < 0.001). Based on anticipated findings, the trend of tourism inflows appears to be improving. The number of visitors is likely to reach 1,045,196.40 in 2024 and is projected to increase even further to 1,156,987.09 in 2028. Confidence intervals show that by 2028, the number of tourists may be as low as 179,546.91 or as high as 4,062,926.89, highlighting the difficulties in estimating long-term numbers. Additionally, the risk of external diversification factors, such as the political and economic environments, which may either facilitate or obstruct the progress of tourism, is typically associated with the expanding gap between the lower and upper confidence limits.
Based on anticipated findings, the trend of tourism inflows appears to be improving. The number of visitors is likely to reach 1,045,196.40 in 2024 and is projected to increase even further to 1,156,987.09 in 2028. Confidence intervals show that by 2028, the number of tourists may be as low as 179,546.91 or as high as 4,062,926.89, highlighting the difficulties in estimating long-term numbers. Additionally, the risk of external diversification factors, such as the political and economic environments, which may either facilitate or obstruct the progress of tourism, is typically associated with the expanding gap between the lower and upper confidence limits.