Forecasting International Tourists Arrival to Nepal Using Autoregressive Integrated Moving Average (ARIMA)

Authors

  • Rajendra Prasad Upadhayaya Ph.D. Scholar, Central Department of Statistics, Tribhuvan University, Kirtipur

DOI:

https://doi.org/10.3126/jjis.v10i01.42614

Keywords:

ARIMA, international tourist, ACF, PACF, Box & Jenkins, forecasting

Abstract

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.

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Published

2021-12-31

How to Cite

Upadhayaya, R. P. (2021). Forecasting International Tourists Arrival to Nepal Using Autoregressive Integrated Moving Average (ARIMA). Janapriya Journal of Interdisciplinary Studies, 10(01), 107–117. https://doi.org/10.3126/jjis.v10i01.42614

Issue

Section

Research Articles