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.
Anaman, K. A., & Looi, C. N. (2000). Economic impact of haze-related air pollution on the tourism industry in Brunei Darussalam. Economic Analysis and Policy, 30 (2), 133-143.
Baggio, R., & Klobas, J. (2011). Quantitative Methods in Tourism: A Handbook. Bristol, UK: Channel View Publications.
Chen, C.-F., Lai, M.-C., & Yeh, C.-C. (2011). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems.
Chen, R., Liang, C.-Y., Hong, W.-C., & Gu, D.-X. (2014). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Review Article.
Chu, F.-L. (2003). Forecasting tourism demand: a cubic polynomial approach. Tourism Management.
Claveria, O., & Torra, S. (2013). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling.
Costa, J., Montenegro, M., & Gomes, J. (2016). What global trends are challenging tourism organizations and destinations today? Worldwide Hospitality and Tourism Themes.
Gunter, U., & Onder, I. (2014). Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tourism Management.
Jala, I. (2016). Pemandu. Retrieved from Tourism: A Key Economic Sector: https://www.pemandu.gov.my/transformation-unplugged-tourism-a-key-economic-sector/
Kumar, M., & Sharma, S. (2016). Forecasting tourist in-flow in South East Asia: A case of Singapore. Tourism & Management Studies.
Liang, Y.-H. (2014). Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan. Computers & Industrial Engineering.
Loganathan, Nanthakumar, & Ibrahim, Y. (2010). Forecasting International Tourism Demand in Malaysia Using Box Jenkins Sarima Application. South Asian Journal of Tourism and Heritage.
Ma, E., Liu, Y., Li, J., & Chen, S. (2015). Anticipating Chinese tourists arrivals in Australia: A time series analysis. Tourism Management Perspectives.
Mamula, M. (2015). Modelling and Forecasting International Tourism Demand – Evaluation of Forecasting Performance . International Journal of Business Administration .
Munan, & Heidi. (2002). Malaysia. New York: Benchmark Books.
Nanthakumar, L., Subramaniam, T., & Kogid, M. (2012). Ia Malaysia truly Asia? Forecasting tourism demand from Asean using SARIMA approach. An International Multidiciplinary Journal of Tourism.
Render, B., Stair, J. M., Hanna, M. E., & Hale, T. S. (2015). Quantitative Analysis for Management. Harlow: Pearson Education Limited.
Rufino, C. C. (2015). Forecasting Monthly Tourist Arrivals from ASEAN+3 countries to the Philippines for 2015-2016 Using SARIMA Noise Modeling.
Shabri, A. (2016). A Hybrid of EEMD and LSSVM-PSO model for Tourist Demand Forecasting. Indian Journal of Science and Technology.
Song, H., & Witt, S. F. (2006). Forecasting International Tourist Flows to Macau.
Song, H., Li, G., Witt, S. F., & Athanasopoulos, G. (2010). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting.
Song, H., Witt, S. F., & Li, G. (2008). The Advanced Econometrics of Tourism Demand. Routledge.
Statistics Canada. (2013). Retrieved from Variance and standard deviation: http://www.statcan.gc.ca/edu/power-pouvoir/ch12/5214891-eng.htm
Stellwagen, E. (2011). A Guide to Forecast Error Measurement Statistics and How to Use Them. Retrieved from Forecast Pro: http://www.forecastpro.com/Trends/forecasting101August2011.html
Time Series Components. (2016). Retrieved from OTexts: https://www.otexts.org/fpp/6/1
Tourism Malaysia. (2016). Retrieved from My Tourism Data: http://mytourismdata.tourism.gov.my/
Tourism Malaysia. (2016). Retrieved from Malaysia's Jan-June 2016 tourist arrivals grow 3.7%: http://www.tourism.gov.my/media/view/malaysia-s-jan-june-2016-tourist-arrivals-grow-3-7
Xu, X., Law, R., Chen, W., & Tang, L. (2016). Forecasting tourism demand by extracting
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.
Copyright: © 2018 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
