Open AccessMalaysia Tourism Demand Forecasting Using Box-Jenkins Approach
Author(s) -
Diyana Izyan Amir Hamzah,
Maria Eleor,
Sabariah Saharan,
Noor Fariza Mohd Hamdan,
Nurul Asmaa Izzati Nohamad
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.30.22366
Subject(s) - box–jenkins , tourism , akaike information criterion , mean absolute percentage error , demand forecasting , mean squared error , absolute deviation , operations research , forecast error , work (physics) , order (exchange) , econometrics , exponential smoothing , computer science , autoregressive integrated moving average , statistics , economics , mathematics , engineering , time series , geography , finance , mechanical engineering , archaeology
Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.