Forecasting model for the number of long stay Japanese tourist arrivals in Chiang Mai
Keywords:
Long Stay Tourism, Classical Decomposition, Seasonal Simple Exponential Smoothing, Box-Jenkins, Combining Forecasts, , Root Mean Square Error (RMSE)
Abstract
Aim: Japan has the oldest average population of any country in the world, and its elderly population is ageing at an alarming rate. As a result, long-stay tourism provides an alternate form of tourism for Japanese retirees. This study aimed to develop a reliable model for predicting the influx of Japanese visitors planning to spend an extended amount of time in Chiang Mai, Thailand.
Method: This study used data collected from the Chiang Mai Immigration Office for 43 months, beginning in January 2014 and ending in July 2017. After that, we divided the information into two groups. The forecasting model was developed using Classical decomposition, Seasonal analysis, simple exponential smoothing, Box-Jenkins, and Combining the first data set covering 36 months from January 2014 to December 2016. The RMSE criterion was used to compare the three earlier methods of forecasting accuracy on a second data set spanning January 2017 to July 2017.
Findings: Combining forecasts was found to be the most appropriate method of forecasting the expected number of long-term Japanese visitors to Chiang Mai.
Implications/Novel Contribution: Chiang Mai, Thailand, is a popular destination for Japanese travellers, but a look through academic journals reveals that nobody has published any research on predicting the number of Japanese visitors staying in the city for an extended period. Therefore, this study substantially advances the existing body of literature.
References
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Çuhadar, M. (2014). Modelling and forecasting inbound tourism demand to Istanbul–a comparative analysis. European Journal of Business and Social Sciences, 2(12), 101–119.
Fahmida, M. S. U., Kulsuma, B. M., & Reza, A. A. T. (2016). Energy balance and its relationship with metabolic disease in Bangladeshi middle-aged women. Journal of Advances in Health and Medical Sciences, 2(2), 61-69. doi:https://doi.org/10.20474/jahms-2.2.3
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Graefe, A., Armstrong, J. S., Jones Jr, R. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections.
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Hejase, H. A., & Assi, A. H. (2012). Time-series regression model for prediction of mean daily global solar radiation in Al-Ain, UAE. ISRN Renewable Energy, 12(6), 45-60. doi:https://doi.org/10.5402/2012/412471
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Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. New York, NY: O Texts.
Khalil, A., Ullah, S., Khan, S. A., Manzoor, S., Gul, A., & Shafiq, M. (2017). Applying time series and a non-parametric approach to predict pattern, variability, and number of rainy days per month. Polish Journal of Environmental Studies, 26(2), 635-642. doi:https://doi.org/10.15244/pjoes/65155
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Komaladewi, R., Mulyana, A., & Jatnika, D. (2017). The representation of culinary experience as the future of Indonesian tourism cases in Bandung city, West Java. International Journal of Business and Economic Affairs, 2(5), 268-275. doi:https://doi.org/10.24088/ijbea-2017-25001
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Kurukulasooriya, N., & Lelwala, E. (2014). Time series behavior of burgeoning international tourist arrivals in Sri Lanka: The post-war experience. Ruhuna Journal of Management and Finance, 1(1), 1-14. doi:https://doi.org/10.4038/ijms.v3i2.13
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Lorde, T., & Moore, W. (2008). Modeling and forecasting the volatility of long-stay tourist arrivals. Tourism Analysis, 13(1), 43-51. doi:https://doi.org/10.3727/108354208784548742
Louw, R., & Saayman, A. (2013). Forecasting tourism demand for South Africa using a single equation causal approach. In Matias, A., Nijkamp, P., and Sarmento, M. (Eds.), Quantitative methods in tourism economics. New York, NY: Springer.
Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K., & Simmons, L. F. (1993). The m2-competition: A real-time judgmentally based forecasting study. International Journal of Forecasting, 9(1), 5-22. doi:https://doi.org/10.1016/0169-2070(93)90044-n
Marascuilo, L. A., & Serlin, R. C. (1988). Statistical methods for the social and behavioral sciences. New York, NY: WH Freeman/Times Books/Henry Holt & Co.
Osman, A. F., & King, L., Maxwell. (2015). A new approach to forecasting based on exponential smoothing with independent regressors (Working paper). Monash Econometrics & Business Statistics, Melbourne, Australia.
Ramanauskaite, E., & Vaisnys, J. R. (2017). Qualitative longitudinal research on lithuanian student migration. Journal of Advances in Humanities and Social Sciences, 3(4), 193-205. doi:https://doi.org/10.20474/jahss-3.4.1
Ramsey, P. H. (1989). Critical values for spearmanâA˘ Zs rank order correlation. ´ Journal of Educational Statistics, 14(3), 245-253. doi:https://doi.org/10.3102/10769986014003245
Saayman, A., & Saayman, M. (2010). Forecasting tourist arrivals in South Africa. Professional Accountant, 10(1), 281-293. doi:https://doi.org/10.4102/ac.v10i1.141
Sawatsuk, B., Darmawijaya, I. G., Ratchusanti, S., & Phaokrueng, A. (2018). Factors determining the sustainable success of community-based tourism: Evidence of good corporate governance of Mae Kam Pong Homestay, Thailand. International Journal of Business and Economic Affairs, 3(1), 13-20. doi:https://doi.org/10.24088/ijbea-2018-31002
Shumway, R. H., & Stoffer, D. S. (2000). Time series analysis and its applications. Studies in Informatics and Control, 9(4), 375-376.
Silva, H. M. S. V., & Madushani, R. A. I. (2017). The impact of human resource competencies of front line employees on tourist arrivals of unclassified hotels in western province, Sri Lanka. Journal of Advanced Research in Social Sciences and Humanities, 2(1), 09-16. doi:https://doi.org/10.26500/jarssh-02-2017-0102
Singh, E. H. (2013). Forecasting tourist inflow in bhutan using seasonal ARIMA. International Journal of Science and Research, 2(9), 242-245.
Sinh, B. D., Nga, V. T., Linh, V. T. H., & Tuan, N. H. (2016). Stakeholder model application in tourism development in Cat Tien, LamDong. Journal of Advanced Research in Social Sciences and Humanities, 1(1),73-95. doi:https://doi.org/10.26500/jarssh-01-2016-0110
Song, H., & Li, G. (2008). Tourism demand modelling and forecastingâA˘Ta review of recent research. ˇ Tourism Management, 29(2), 203-220. doi:https://doi.org/10.1016/j.tourman.2007.07.016
Song, H., Witt, S. F., Wong, K. F., & Wu, D. C. (2009). An empirical study of forecast combination in tourism. Journal of Hospitality and Tourism Research, 33(1), 3-29. doi:https://doi.org/10.1177/1096348008321366
Wang, Y., & Lim, C. (2005). Using time series models to forecast tourist flows. In Proceedings of the 2005 International Conference on Simulation and Modelling, Kula Lampu, Malaysia.
Yoshida, E. (2015). International retirement migration in Thailand: From the perspective of welfare and social participation. Retrieved from https://bit.ly/2AmffMQ (accessed on 14 June, 2016)
Yürekli, K., Kurunç, A., & Öztürk, F. (2005). Testing the residuals of an ARIMA model on the cekerek stream watershed in Turkey. Turkish Journal of Engineering and Environmental Sciences, 29(2), 61-74.
Berenson, M. L., & Levine, D. M. (1988). Applied statistics: A first course. New York, NY: Prentice-Hall, Inc.
Cabinet Office Government of Japan. (2016). Annual report on the aging society: 2015. Retrieved from https://bit.ly/2rZ9FLD (accessed on 14 July, 2017)
Chan, H. T. (2018). What is the problem represented to be: A research methodology for analysing Australias skilled migration policy. International Journal of Business and Economic Affairs, 3(1), 21-32. doi:https://doi.org/10.24088/ijbea-2018-31003
Chu, F. L. (1998). Forecasting tourism: A combined approach. Tourism Management, 19(6), 515-520. doi:https://doi.org/10.1016/s0261-5177(98)00053-3
Çuhadar, M. (2014). Modelling and forecasting inbound tourism demand to Istanbul–a comparative analysis. European Journal of Business and Social Sciences, 2(12), 101–119.
Fahmida, M. S. U., Kulsuma, B. M., & Reza, A. A. T. (2016). Energy balance and its relationship with metabolic disease in Bangladeshi middle-aged women. Journal of Advances in Health and Medical Sciences, 2(2), 61-69. doi:https://doi.org/10.20474/jahms-2.2.3
Gajic, T., Vujko, A., & Papi ´ c Blagojevi ´ c, N. (2015). Forecasting tourist arrivals in Novi sad by using the ARIMA ´ model. In Second International Conference Higher Education in Function of Development of Tourism in Serbia and Western Balkans, Uzice, Serbia.
Goh, C., & Law, R. (2011). The methodological progress of tourism demand forecasting: A review of related literature. Journal of Travel & Tourism Marketing, 28(3), 296-317. doi:https://doi.org/10.1080/10548408.2011.562856
Graefe, A., Armstrong, J. S., Jones Jr, R. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections.
International Journal of Forecasting, 30(1), 43-54. doi:https://doi.org/10.1016/j.ijforecast.2013.02.005
Hejase, H. A., & Assi, A. H. (2012). Time-series regression model for prediction of mean daily global solar radiation in Al-Ain, UAE. ISRN Renewable Energy, 12(6), 45-60. doi:https://doi.org/10.5402/2012/412471
Hongsranagon, P. (2005). Advisory facilities for long-stay Japanese senior travelers in Chiangmai. Journal of Humanities, 8(2), 58-66. doi:https://doi.org/10.5367/000000006778493628
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. New York, NY: O Texts.
Khalil, A., Ullah, S., Khan, S. A., Manzoor, S., Gul, A., & Shafiq, M. (2017). Applying time series and a non-parametric approach to predict pattern, variability, and number of rainy days per month. Polish Journal of Environmental Studies, 26(2), 635-642. doi:https://doi.org/10.15244/pjoes/65155
Khaokhrueamuang, A. (2014). The commodification of rurality in Mae Kam Pong village for Japanese long-stay tourism in Chiang Mai, Thailand. In Proceedings of the General Meeting of the Association of Japanese Geographers Annual Meeting of the Association of Japanese Geographers, Tokyo, Japan.
Komaladewi, R., Mulyana, A., & Jatnika, D. (2017). The representation of culinary experience as the future of Indonesian tourism cases in Bandung city, West Java. International Journal of Business and Economic Affairs, 2(5), 268-275. doi:https://doi.org/10.24088/ijbea-2017-25001
Kongprasert, T. (2013). Factors influencing types of Japanese tourists in Chiang Mai (Unpublished master’s thesis). Chulalongkorn University, Bangkok, Thailand.
Kurukulasooriya, N., & Lelwala, E. (2014). Time series behavior of burgeoning international tourist arrivals in Sri Lanka: The post-war experience. Ruhuna Journal of Management and Finance, 1(1), 1-14. doi:https://doi.org/10.4038/ijms.v3i2.13
Lin, C. J., Chen, H. F., & Lee, T. S. (2011). Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: Evidence from Taiwan. International Journal of Business Administration, 2(2), 14. doi:https://doi.org/10.5430/ijba.v2n2p14
Loftis, J. C., McBride, G. B., & Ellis, J. C. (1991). Considerations of scale in water quality monitoring and data analysis 1. Journal of the American Water Resources Association, 27(2), 255-264. doi:https://doi.org/10.1111/j.1752-1688.1991.tb03130.x
Lorde, T., & Moore, W. (2008). Modeling and forecasting the volatility of long-stay tourist arrivals. Tourism Analysis, 13(1), 43-51. doi:https://doi.org/10.3727/108354208784548742
Louw, R., & Saayman, A. (2013). Forecasting tourism demand for South Africa using a single equation causal approach. In Matias, A., Nijkamp, P., and Sarmento, M. (Eds.), Quantitative methods in tourism economics. New York, NY: Springer.
Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K., & Simmons, L. F. (1993). The m2-competition: A real-time judgmentally based forecasting study. International Journal of Forecasting, 9(1), 5-22. doi:https://doi.org/10.1016/0169-2070(93)90044-n
Marascuilo, L. A., & Serlin, R. C. (1988). Statistical methods for the social and behavioral sciences. New York, NY: WH Freeman/Times Books/Henry Holt & Co.
Osman, A. F., & King, L., Maxwell. (2015). A new approach to forecasting based on exponential smoothing with independent regressors (Working paper). Monash Econometrics & Business Statistics, Melbourne, Australia.
Ramanauskaite, E., & Vaisnys, J. R. (2017). Qualitative longitudinal research on lithuanian student migration. Journal of Advances in Humanities and Social Sciences, 3(4), 193-205. doi:https://doi.org/10.20474/jahss-3.4.1
Ramsey, P. H. (1989). Critical values for spearmanâA˘ Zs rank order correlation. ´ Journal of Educational Statistics, 14(3), 245-253. doi:https://doi.org/10.3102/10769986014003245
Saayman, A., & Saayman, M. (2010). Forecasting tourist arrivals in South Africa. Professional Accountant, 10(1), 281-293. doi:https://doi.org/10.4102/ac.v10i1.141
Sawatsuk, B., Darmawijaya, I. G., Ratchusanti, S., & Phaokrueng, A. (2018). Factors determining the sustainable success of community-based tourism: Evidence of good corporate governance of Mae Kam Pong Homestay, Thailand. International Journal of Business and Economic Affairs, 3(1), 13-20. doi:https://doi.org/10.24088/ijbea-2018-31002
Shumway, R. H., & Stoffer, D. S. (2000). Time series analysis and its applications. Studies in Informatics and Control, 9(4), 375-376.
Silva, H. M. S. V., & Madushani, R. A. I. (2017). The impact of human resource competencies of front line employees on tourist arrivals of unclassified hotels in western province, Sri Lanka. Journal of Advanced Research in Social Sciences and Humanities, 2(1), 09-16. doi:https://doi.org/10.26500/jarssh-02-2017-0102
Singh, E. H. (2013). Forecasting tourist inflow in bhutan using seasonal ARIMA. International Journal of Science and Research, 2(9), 242-245.
Sinh, B. D., Nga, V. T., Linh, V. T. H., & Tuan, N. H. (2016). Stakeholder model application in tourism development in Cat Tien, LamDong. Journal of Advanced Research in Social Sciences and Humanities, 1(1),73-95. doi:https://doi.org/10.26500/jarssh-01-2016-0110
Song, H., & Li, G. (2008). Tourism demand modelling and forecastingâA˘Ta review of recent research. ˇ Tourism Management, 29(2), 203-220. doi:https://doi.org/10.1016/j.tourman.2007.07.016
Song, H., Witt, S. F., Wong, K. F., & Wu, D. C. (2009). An empirical study of forecast combination in tourism. Journal of Hospitality and Tourism Research, 33(1), 3-29. doi:https://doi.org/10.1177/1096348008321366
Wang, Y., & Lim, C. (2005). Using time series models to forecast tourist flows. In Proceedings of the 2005 International Conference on Simulation and Modelling, Kula Lampu, Malaysia.
Yoshida, E. (2015). International retirement migration in Thailand: From the perspective of welfare and social participation. Retrieved from https://bit.ly/2AmffMQ (accessed on 14 June, 2016)
Yürekli, K., Kurunç, A., & Öztürk, F. (2005). Testing the residuals of an ARIMA model on the cekerek stream watershed in Turkey. Turkish Journal of Engineering and Environmental Sciences, 29(2), 61-74.
Published
2018-08-27
Section
Articles
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