International Journal of Academic Research in Business and Social Sciences

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Forecasting International Tourist Arrivals in Penang using Time Series Model

Open access

Shahirah Khairudin, Nursyatiella Ahmad, Azura Razali, Ahmad Zia Ul-Saufie Mohamad Japeri, Azila Binti Azmi

Pages 38-59 Received: 27 Oct, 2018 Revised: 21 Nov, 2018 Published Online: 31 Dec, 2018

http://dx.doi.org/10.46886/IJARBSS/v8-i16/5117
Tourism forecasting plays an important role for the future development of tourism industry to accelerate the economic growth. The appropriate forecast in tourism gives benefit to both public and private sectors as the information concerning the future tourism flows is important to tourism stakeholder. However, there is no appropriate forecasting model employed in Penang. Hence, the purpose of this study is to determine the best model in forecasting international tourist arrivals in Penang for 2016–2017. Secondary data on tourist arrival to Penang for 2010-2015 was obtained from Ministry of Tourism and Culture Malaysia. The data were analysed using software QM for Windows. Trend projection model and trend projection with seasonal effect model are used in this study. The accuracy of these two models are determined using mean absolute percentage error (MAPE) and the results show that trend projection with seasonal effect model outperformed trend projection model. MAPE results are between 7.7% and 33.6%. Therefore, trend projection with seasonal effect model will be proposed to be employed in forecasting international tourist arrivals in Penang. Adequate data is important in managing resources to avoid scarcity, or over spending and excessive waste in resources. Thus, this study will be beneficial to the local authority, industry players, as well as other tourism stakeholders in Penang.
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In-Text Citation: (Khairudin, Ahmad, Razali, Japeri, & Azmi, 2018)
To Cite this Article: Khairudin, S., Ahmad, N., Razali, A., Japeri, A. Z. U.-S. M., & Azmi, A. B. (2018). Forecasting International Tourist Arrivals in Penang using Time Series Model. International Journal of Academic Research in Business and Social Sciences, 8(16), 38–59.