Develop Tourism Demand Forecasting Model using Artificial Neural Network and ARIMA

Abstract

Tourism demand plays a significant role in development of tourism sector. As Malaysia is a multiracial country with a rich heritage, it consists of 13 states and three federal territories. Malaysia is presenting an average 26.13 millions of tourist arrivals with the average RM69.84 billions of tourist receipts. The performance of Malaysia tourism sector is in prospect with the improvement of every field which is related to tourism sector. Therefore, to boost the improvement and development, the researcher proposes to identify the best forecasting technique for forecast the tourism demand and choose Sabah as the research place in this study because of its motley of cultures. On top of that, Sabah tourism is one of the major contributors to Sabah’s economy. There are two forecasting techniques will be applied which are ANN approach and ARIMA approach. The results will be compared for purpose to identify the best forecasting technique for forecast the tourism demand of Sabah, Malaysia. Besides, the interview conducted in order to comprehend the reasons that influence tourist arrivals in Sabah. Based on the results of the study, the researcher concluded that the Artificial Neural Network is the best forecasting technique for forecast the number of tourist arrivals in Sabah for the future. This information provided an outline for develop tourism policy to increase tourism demand and Sabah’s economic status. From this study, it will effectively give a boost to growth the nation’s economy indirectly