General Letters in Mathematics

Volume 7 - Issue 2 (6) | PP: 100 - 107 Language : English
DOI : https://doi.org/10.31559/glm2019.7.2.6
672
30

Forecasting international tourist arrivals in zanzibar using box – jenkins SARIMA model

Zulkifr Abdallah Msofe ,
Maurice Chakusaga Mbago
Received Date Revised Date Accepted Date Publication Date
11/11/2019 9/12/2019 27/12/2019 22/1/2020
Abstract
The arrival of international tourists contributes to the generation of foreign currencies and creates employment opportunities to the local people. Modelling and forecasting tourist arrivals plays a major role in tourism planning and marketing and therefore crucial for policy decision-making towards sustainable tourism development. In this paper an attempt has been made to forecast international tourist arrivals in Zanzibar using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Data from January 1995 to December 2017 covering 276 observations were used. The SARIMA (1, 1, 1) × (1, 1, 2)12 model was found to be the best fitted model on the basis of Akaike`s Information Criterion (AIC). The adequacy of the fitted model was confirmed by Ljung-Box test statistic and the model was used to generate monthly forecasts from January 2018 to December 2019 with 95% confidence interval. The forecasting performances of candidate models were evaluated on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The forecasts indicate that the number of tourists visiting Zanzibar is likely to keep on increasing with seasonal pattern similar to that of the original data.


How To Cite This Article
, Z. A. M. & , M. C. M. (2020). Forecasting international tourist arrivals in zanzibar using box – jenkins SARIMA model . General Letters in Mathematics, 7 (2), 100-107, 10.31559/glm2019.7.2.6

Copyright © 2024, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.