Forecasting tourism arrival in Panama using ARIMA models
Abstract
The tourism industry stands out as a significant source of revenue. Beyond driving economic growth and job creation, tourism also plays a key role in stimulating infrastructures development and promoting services tourist. This study aimed to forecast tourist arrivals in Panama using ARIMA models. Monthly data on tourist arrivals to Panama were collected. The results show that the ARIMA model provides reasonable and useful forecasts for tourist arrivals in Panama, with an acceptable level of accuracy. Therefore, in conclusion, the results of this study become significant for the strategic planning of tourism in Panama, in response to the dynamics of this important sector of the national economy.
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References
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Janjua, L. R., Muhammad, F., Sukjai, P., Rehman, A., & Yu, Z. (2021). Impact of COVID-19 pandemic on logistics performance, economic growth and tourism industry of Thailand: An empirical forecasting using ARIMA. Brazilian Journal of Operations and Production Management, 18(2). https://doi.org/10.14488/BJOPM.2021.001
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Kim, J. H., Wong, K., Athanasopoulos, G., & Liu, S. (2011). Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals. International Journal of Forecasting, 27(3), 887-991. https://doi.org/10.1016/j.ijforecast.2010.02.014
Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410-423. https://doi.org/10.1016/j.annals.2019.01.014
Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, 102912. https://doi.org/10.1016/j.annals.2020.102912
Liu, H., Liu, Y., Wang, Y., & Pan, C. (2018). Hot topics and emerging trends in tourism forecasting research: A scientometric review. Tourism Economics, 25(3), 448-468. https://doi.org/10.1177/1354816618810564
Loor, L., Plaza, N., y Medina, Z. (2021). Turismo comunitario en Ecuador: Apuntes en tiempos de pandemia. Revista de Ciencias Sociales (Ve), XXVII(1), 265-277. https://doi.org/10.31876/rcs.v27i1.35312
Moritz, S., Sardá, A., Bartz-Beielstein, T., Zaefferer, M., & Stork, J. (2015). Comparison of different Methods for Univariate Time Series Imputation in R. ArXiv:1510.03924. https://doi.org/10.48550/arXiv.1510.03924
Naranjo, M. R., & Martínez, M. D. L. A. (2022). Reflexiones teóricas sobre la demanda turística global: Incidencia en la gestión y comercialización turística. Revista de Ciencias Sociales (Ve), XXVIII(E-5), 359-375. https://doi.org/10.31876/rcs.v28i.38169
Nissan, E., Galindo, M.-A., & Méndez, M. T. (2011). Relationship between tourism and economic growth. The Service Industries Journal, 31(10), 1567-1572. https://doi.org/10.1080/02642069.2010.485636
Nugra, M. A., Illescas, W. H., Cuadros, P. A., y Valdivia, R. A. (2021). Turismo minero en Yanacocha: Una alternativa de desarrollo para la región de Cajamarca-Perú. Revista de Ciencias Sociales (Ve), XXVII(1), 278-289. https://doi.org/10.31876/rcs.v27i1.35313
Ospina, R., Gondim, J. A. M., Leiva, V., & Castro, C. (2023). An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. Mathematics, 11(14), 3069. https://doi.org/10.3390/math11143069
Pan, B., & Yang, Y. (2017). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), 957-970. https://doi.org/10.1177/0047287516669050
Park, J., Müller, J., Arora, B., Faybishenko, B., Pastorello, G., Varadharajan, C., Sahu, R., & Agarwal, D. (2023). Long-term missing value imputation for time series data using deep neural networks. Neural Computing and Applications, 35(12), 9071-9091. https://doi.org/10.1007/S00521-022-08165-6
Peña, D. (2010). Análisis de series temporales. Alianza Editorial.
Phan, T.-T.-H., Poisson, É., Lefebvre, A., & Bigand, A. (2020). Dynamic time warping-based imputation for univariate time series data. Pattern Recognition Letters, 139, 139-147. https://doi.org/10.1016/j.patrec.2017.08.019
Silva, F., & Roque, M. (2024). Building the framework for sustainable tourism in Principe Island. Tourism and Hospitality, 5(1), 225-236. https://doi.org/10.3390/tourhosp5010015
Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362. https://doi.org/10.1016/j.annals.2018.12.001
Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern econometric approaches. Taylor & Francis.
Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10. https://doi.org/10.1016/j.tourman.2018.07.010
Tovmasyan, G. (2023). Factors that influence domestic tourism demand: Evidence from Armenia. Economics & Sociology, 16(2), 75-88. https://www.economics-sociology.eu/?961,en_factors-that-influence-domestic-tourism-demand-evidence-from-armenia
Varzakas, I.-P., & Metaxas, T. (2024). Pandemic and Economy: An Econometric Analysis Investigating the Impact of COVID-19 on the Global Tourism Market. Tourism and Hospitality, 5(2), 290-303. https://doi.org/10.3390/tourhosp5020019
Wong, K. K. F., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068-1078. https://doi.org/10.1016/J.TOURMAN.2006.08.003
World Trade Organization - WTO (2022). World Trade Report 2022: Climate change and international trade. WTO. https://www.wto.org/english/res_e/publications_e/wtr22_e.htm
World Trade Organization - WTO (2023). World Trade Report 2023: Re-globalization for a secure, inclusive and sustainable future. WTO. https://www.wto.org/english/res_e/publications_e/wtr23_e.htm
World Travel and Tourism Council - WTTC (24 de mayo de 2023). Contribución del sector de viajes y turismo de Panamá representará el 16.4% del PIB nacional. World Travel and Tourism Council. https://wttc.org/news/contribucion-del-sector-de-viajes-y-turismo-de-panama-representara-el-16-4-del-pib-nacional
Zhang, C., Wang, S., Sun, S., & Wei, Y. (2020). Knowledge mapping of tourism demand forecasting research. Tourism Management Perspectives, 35, 100715. https://doi.org/10.1016/j.tmp.2020.100715
Zhou, F., Zheng, T., Schrier, T., & Farrish, J. (2022). Examining the impact of China’s corruption crackdown: A forecast for Macau’s tourism and gaming industry. Tourism and Hospitality, 3(3), 752-764. https://doi.org/10.3390/tourhosp3030046
Anzaldúa-Soulé, K. R., Almazán-Adame, A. A., Lorenzana, O., & Saldaña, M. (2021). Potencial paisajístico de la Laguna de Coyuca de Benítez: Detonante de productos sustentables en Acapulco-México. Revista de Ciencias Sociales (Ve), XXVII(2), 80-97. https://doi.org/10.31876/rcs.v27i2.35890
Athanasopoulos, G., Hyndman, R. J., Song, H., & Wu, D. C. (2011). The tourism forecasting competition. International Journal of Forecasting, 27(3), 822-844. https://doi.org/10.1016/j.ijforecast.2010.04.009
Autoridad de Turismo de Panamá (2023). Análisis del Desempeño Turísticos. Autoridad de Turismo de Panamá. https://www.atp.gob.pa/estadisticas-e-informacion-del-mercado-2/
Baldigara, T., & Mamula, M. (2012). Tourism statistics in Croatia: Present status and future challenges. Procedia - Social and Behavioral Sciences, 44, 53-61. https://doi.org/10.1016/j.sbspro.2012.05.004
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. Wiley.
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day.
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). Brits: Bidirectional recurrent imputation for time series. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi & R. Garnett (Eds.) Advances in Neural Information Processing Systems, 31 (paper 3408). Curran Associates, Inc.
Denk, M., & Weber, M. (2011). Avoid filling Swiss cheese with whipped cream: Imputation techniques and evaluation procedures for cross-country time series. IMF Working Paper No. 11/151. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1886902
Du Preez, J., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting, 19(3), 435-451. https://doi.org/10.1016/S0169-2070(02)00057-2
Gunter, U., & Önder, I. (2015). Forecasting international city tourism demand for Paris: Accuracy of uni-and multivariate models employing monthly data. Tourism Management, 46, 123-135. https://doi.org/10.1016/j.tourman.2014.06.017
Hanafiah, M. H., Hasan, M. R., & Som, A. P. M. (2022). Managing modern Muslim travellers: Emerging trends and issues for Islamic tourism destinations. Tourism and Hospitality, 3(4), 908-918. https://doi.org/10.3390/tourhosp3040058
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Janjua, L. R., Muhammad, F., Sukjai, P., Rehman, A., & Yu, Z. (2021). Impact of COVID-19 pandemic on logistics performance, economic growth and tourism industry of Thailand: An empirical forecasting using ARIMA. Brazilian Journal of Operations and Production Management, 18(2). https://doi.org/10.14488/BJOPM.2021.001
Jayasundara, C. L. (2019). Forecasting monthly tourist arrivals to Sri Lanka [Master’s theses, University of Moratuwa]. http://dl.lib.mrt.ac.lk/handle/123/15909
Kaya, A., & Turkoglu, I. (2021). Comparison of clustering performances of missing data imputation methods. Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-6). Elazig, Turkey. https://doi.org/10.1109/ASYU52992.2021.9599080
Kim, J. H., Wong, K., Athanasopoulos, G., & Liu, S. (2011). Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals. International Journal of Forecasting, 27(3), 887-991. https://doi.org/10.1016/j.ijforecast.2010.02.014
Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410-423. https://doi.org/10.1016/j.annals.2019.01.014
Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, 102912. https://doi.org/10.1016/j.annals.2020.102912
Liu, H., Liu, Y., Wang, Y., & Pan, C. (2018). Hot topics and emerging trends in tourism forecasting research: A scientometric review. Tourism Economics, 25(3), 448-468. https://doi.org/10.1177/1354816618810564
Loor, L., Plaza, N., y Medina, Z. (2021). Turismo comunitario en Ecuador: Apuntes en tiempos de pandemia. Revista de Ciencias Sociales (Ve), XXVII(1), 265-277. https://doi.org/10.31876/rcs.v27i1.35312
Moritz, S., Sardá, A., Bartz-Beielstein, T., Zaefferer, M., & Stork, J. (2015). Comparison of different Methods for Univariate Time Series Imputation in R. ArXiv:1510.03924. https://doi.org/10.48550/arXiv.1510.03924
Naranjo, M. R., & Martínez, M. D. L. A. (2022). Reflexiones teóricas sobre la demanda turística global: Incidencia en la gestión y comercialización turística. Revista de Ciencias Sociales (Ve), XXVIII(E-5), 359-375. https://doi.org/10.31876/rcs.v28i.38169
Nissan, E., Galindo, M.-A., & Méndez, M. T. (2011). Relationship between tourism and economic growth. The Service Industries Journal, 31(10), 1567-1572. https://doi.org/10.1080/02642069.2010.485636
Nugra, M. A., Illescas, W. H., Cuadros, P. A., y Valdivia, R. A. (2021). Turismo minero en Yanacocha: Una alternativa de desarrollo para la región de Cajamarca-Perú. Revista de Ciencias Sociales (Ve), XXVII(1), 278-289. https://doi.org/10.31876/rcs.v27i1.35313
Ospina, R., Gondim, J. A. M., Leiva, V., & Castro, C. (2023). An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. Mathematics, 11(14), 3069. https://doi.org/10.3390/math11143069
Pan, B., & Yang, Y. (2017). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), 957-970. https://doi.org/10.1177/0047287516669050
Park, J., Müller, J., Arora, B., Faybishenko, B., Pastorello, G., Varadharajan, C., Sahu, R., & Agarwal, D. (2023). Long-term missing value imputation for time series data using deep neural networks. Neural Computing and Applications, 35(12), 9071-9091. https://doi.org/10.1007/S00521-022-08165-6
Peña, D. (2010). Análisis de series temporales. Alianza Editorial.
Phan, T.-T.-H., Poisson, É., Lefebvre, A., & Bigand, A. (2020). Dynamic time warping-based imputation for univariate time series data. Pattern Recognition Letters, 139, 139-147. https://doi.org/10.1016/j.patrec.2017.08.019
Silva, F., & Roque, M. (2024). Building the framework for sustainable tourism in Principe Island. Tourism and Hospitality, 5(1), 225-236. https://doi.org/10.3390/tourhosp5010015
Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362. https://doi.org/10.1016/j.annals.2018.12.001
Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern econometric approaches. Taylor & Francis.
Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10. https://doi.org/10.1016/j.tourman.2018.07.010
Tovmasyan, G. (2023). Factors that influence domestic tourism demand: Evidence from Armenia. Economics & Sociology, 16(2), 75-88. https://www.economics-sociology.eu/?961,en_factors-that-influence-domestic-tourism-demand-evidence-from-armenia
Varzakas, I.-P., & Metaxas, T. (2024). Pandemic and Economy: An Econometric Analysis Investigating the Impact of COVID-19 on the Global Tourism Market. Tourism and Hospitality, 5(2), 290-303. https://doi.org/10.3390/tourhosp5020019
Wong, K. K. F., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068-1078. https://doi.org/10.1016/J.TOURMAN.2006.08.003
World Trade Organization - WTO (2022). World Trade Report 2022: Climate change and international trade. WTO. https://www.wto.org/english/res_e/publications_e/wtr22_e.htm
World Trade Organization - WTO (2023). World Trade Report 2023: Re-globalization for a secure, inclusive and sustainable future. WTO. https://www.wto.org/english/res_e/publications_e/wtr23_e.htm
World Travel and Tourism Council - WTTC (24 de mayo de 2023). Contribución del sector de viajes y turismo de Panamá representará el 16.4% del PIB nacional. World Travel and Tourism Council. https://wttc.org/news/contribucion-del-sector-de-viajes-y-turismo-de-panama-representara-el-16-4-del-pib-nacional
Zhang, C., Wang, S., Sun, S., & Wei, Y. (2020). Knowledge mapping of tourism demand forecasting research. Tourism Management Perspectives, 35, 100715. https://doi.org/10.1016/j.tmp.2020.100715
Zhou, F., Zheng, T., Schrier, T., & Farrish, J. (2022). Examining the impact of China’s corruption crackdown: A forecast for Macau’s tourism and gaming industry. Tourism and Hospitality, 3(3), 752-764. https://doi.org/10.3390/tourhosp3030046
Published
2025-08-26
How to Cite
Cubilla-Montilla, M., Frende Vega, M. de los Ángeles, Cruz, C. E., & Muñoz Agrazal, A. O. (2025). Forecasting tourism arrival in Panama using ARIMA models. Revista De Ciencias Sociales, 31(3), 21-37. https://doi.org/10.31876/rcs.v31i3.44267
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