A hybrid approach on tourism demand forecasting

• Main Authors: Nor, M. E., A. I. M., Nurul, Rusiman, M. S.
• Format: Conference or Workshop Item
• Language: English
• Published: 2017
• Subjects: H Social Sciences (General)
• Online Access: http://eprints.uthm.edu.my/7007/1/P9903_455b98263296c2429a1b772c751a40db.pdf; http://eprints.uthm.edu.my/7007/; https://doi.org/10.1088/1742-6596/995/1/012034

Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models