Open AccessForecasting International Tourists Arrival to Nepal Using Autoregressive Integrated Moving Average (ARIMA)
Author(s) -
Rajendra Prasad Upadhayaya
Publication year - 2021
Publication title -
janapriya journal of interdisciplinary studies/janapriya journal of interdisciplinary studies
Language(s) - English
Resource type - Journals
eISSN - 2773-8000
pISSN - 2362-1516
DOI - 10.3126/jjis.v10i01.42614
Subject(s) - autoregressive integrated moving average , partial autocorrelation function , box–jenkins , autocorrelation , statistics , autoregressive model , econometrics , mathematics , moving average , time series
This paper analyses the tourist arrival in Nepal during the last 56 years using various quantitative techniques. By the use of autoregressive integrated moving average (ARIMA) model in projecting the tourist arrival in Nepal based on historical data. Box & Jenkins methodology has been employed to forecast a variable using a database of international tourist arrival. Autocorrelation function (ACF) and partial autocorrelation function (PACF) have been used along with Ljung-Box Statistics for the test of stationary. Parameters p, d, q has been identified based on different diagnostic test statistics such as stationary R2, MAPE, Normalized BIC, ACF, and PACF. Conclusively, the model ARIMA (1, 1, 1) has been selected with the least BIC and tourist arrival forecasted to be 1,322,000 (LCL: 919,090 and UCL: 1,724,909) in the year 2025.