A Seasonal Autoregressive Integrated Moving Average (SARIMA) Model to Forecasting Tourist Arrival in the Philippines: A Case Study in Moalboal, Cebu (Philippines)
DOI:
https://doi.org/10.32871/rmrj2008.01.05Keywords:
Tourism demand forecasting, time series forecasting, SARIMA, stochastic processes, seasonal time seriesAbstract
Forecasting plays a critical part in implementing effective tourism management strategies. However, the role of tourism forecasting is not extensively studied in the Philippines, which is a key tourism destination in Southeast Asia. To address such gap, this paper explores the dynamics of tourist demand in the Philippines through a case study. It illustrates the tourist arrival using a SARIMA model. Results show that the adopted methodology was able to capture the dynamics of the tourist demand in the Philippines. By providing lenses to the Philippine tourism case, this paper would help shed light to the gaps in the literature’s current understanding of tourism in the Philippines. Moreover, these findings would be beneficial for stakeholders in shaping policies, strategies, and other tourism initiatives.
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Automated Box–Jenkins forecasting tool
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Tourism Research, 83, 102923.
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monthly headline consumer price index
(HCPI) through seasonal Box-Jenkins
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tourist arrivals using denoising and potential
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102943.
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tourism demand after allowance for
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the Philippines for 2015-2016 using SARIMA
Noise Modeling. In Presented at 2016 DLSU
Research Congress.
Shadwick, E., Trull, T., & Tilbrook, B. (2015).
Seasonality of biological and physical
controls on surface ocean CO2 from hourly
observations at the Southern Ocean
Time Series site south of Australia. Global
Biogeochemical Cycles, 29(2), 223-238.
Shumway, R. H., & Stoffer, D. S. (2017). ARIMA
models. In Time series analysis and its
applications (pp. 75-163). Springer, Cham.
Song, H., & Li, G. (2008). Tourism demand modelling
and forecasting—A review of recent
research. Tourism management, 29(2), 203-
220.
Song, H., Qiu, R. T., & 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.
Stewart, K. G. (2005). Introduction to applied
econometrics. Belmont, CA: Thomson Brooks/
Cole.
Ãœlke, V., Sahin, A., & Subasi, A. (2018). A comparison
of time series and machine learning models
for inflation forecasting: empirical evidence
from the USA. Neural Computing and
Applications, 30(5), 1519-1527.
WISCONSIN UNIV MADISON DEPT OF STATISTICS,
Box,G. E. P., & Jenkins,G. M. (1970). Time series
analysis forecasting and control.
Rivero, A. (2020). Hybrid SVR-SARIMA model
for tourism forecasting using PROMETHEE
II as a selection methodology: a Philippine
scenario. Journal of Tourism Futures.
Anvari, S., Tuna, S., Canci, M., &Turkay, M. (2016).
Automated Box–Jenkins forecasting tool
with an application for passenger demand
in urban rail systems. Journal of Advanced
Transportation, 50(1), 25-49
Ashley, R. (2003). Statistically significant forecasting
improvements: how much out-of-sample
data is likely necessary? International Journal
of Forecasting, 19, 229-239.
Baldigara, T., & Mamula, M. (2015). Modeling
international tourism demand using
seasonal ARIMA models. Tourism and
hospitality management, 21(1), 19-31
Bi, J. W., Liu, Y., & Li, H. (2020). Daily tourism volume
forecasting for tourist attractions. Annals of
Tourism Research, 83, 102923.
Brockwell, P. J., & Davis, R. A. (2016). Introduction to
time series and forecasting. Springer.
Callies, J., Ferrari, R., Klymak, J., & Gula, J. (2015).
Seasonality in submesoscale turbulence.
Nature communications, 6, 6862. https://doi.
org/10.1038/ncomms7862
Department of Tourism (2018). Visitor arrivals to the
Philippines by country of residence: January –
December 2017. Retrieved from http://www.
tourism.gov.ph/industry_performance/
December2018/January%20December%20
2018%20Table%202.pdf
Etulle-Tapanan, H. (2015). Path Analysis of Climate
and Tourism to the Economic Growth in
the Philippines. Recoletos Multidisciplinary
Research Journal, 3(1). https://doi.
org/10.32871/rmrj1503.01.03
Fernandez, F. R., Po III, R., Montero, N., & Addawe, R.
(2017, November). Prediction of South China
sea level using seasonal ARIMA models. In
AIP Conference Proceedings 1905(1), 050018.
AIP Publishing LLC.
Gairaa, K., Khellaf, A., Messlem, Y., & Chellali, F.
(2016). Estimation of the daily global solar
radiation based on Box–Jenkins and ANN
models: A combined approach. Renewable
and Sustainable Energy Reviews, 57, 238-249
Geurts, M., Buchman, T., & Ibrahim, I. (1976). Use of
the Box-Jenkins approach to forecast tourist
arrivals. Journal of Travel Research, 14(4), 5-8.
Ghalehkhondabi, I., Ardjmand, E., Young, W.A. &
Weckman, G.R., (2019). A review of demand
forecasting models and methodological
developments within tourism and passenger
transportation industry. Journal of Tourism
Futures, 5(1), 75-93. https://doi.org/10.1108/
JTF-10-2018-0061
Hyndman, R., & Koehler, A. (2006). Another look at
measures of forecast accuracy. International
journal of forecasting, 22(4), 679-688.
Jackson, E., Sillah, A., &Tamuke, E. (2018). Modeling
monthly headline consumer price index
(HCPI) through seasonal Box-Jenkins
methodology. International Journal of
Sciences, 7(1), 51-56.
Li, C., Ge, P., Liu, Z., & Zheng, W. (2020). Forecasting
tourist arrivals using denoising and potential
factors. Annals of Tourism Research, 83,
102943.
Liang, Y. (2014). Forecasting models for Taiwanese
tourism demand after allowance for
Mainland China tourists visiting Taiwan.
Computers & Industrial Engineering, 74, 111-
119.
Maliberan, R. M. E. (2019). Forecasting tourist
arrival in the Province of Surigao del Sur,
Philippines using time sesries analysis.
JOIV: International Journal on Informatics
Visualization, 3(3), 255-261.
Philippine Statistcs Authority (2018). Contribution
of Tourism to the Economy is 12.2 Percent in
2017. Republic of the Philippines: Philippine
Statistics Authority. Retrieved from https://
psa.gov.ph/content/contribution-tourismeconomy-122-percent-2017
Rufino, C. C. (2011). Forecasting international
demand for Philippine Tourism. DLSU
Business & Economics Review, 21(1), 61-76.
Rufino, C. C. (2016, March). Forecasting monthly
tourist arrivals from ASEAN+ 3 countries to
the Philippines for 2015-2016 using SARIMA
Noise Modeling. In Presented at 2016 DLSU
Research Congress.
Shadwick, E., Trull, T., & Tilbrook, B. (2015).
Seasonality of biological and physical
controls on surface ocean CO2 from hourly
observations at the Southern Ocean
Time Series site south of Australia. Global
Biogeochemical Cycles, 29(2), 223-238.
Shumway, R. H., & Stoffer, D. S. (2017). ARIMA
models. In Time series analysis and its
applications (pp. 75-163). Springer, Cham.
Song, H., & Li, G. (2008). Tourism demand modelling
and forecasting—A review of recent
research. Tourism management, 29(2), 203-
220.
Song, H., Qiu, R. T., & 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.
Stewart, K. G. (2005). Introduction to applied
econometrics. Belmont, CA: Thomson Brooks/
Cole.
Ãœlke, V., Sahin, A., & Subasi, A. (2018). A comparison
of time series and machine learning models
for inflation forecasting: empirical evidence
from the USA. Neural Computing and
Applications, 30(5), 1519-1527.
WISCONSIN UNIV MADISON DEPT OF STATISTICS,
Box,G. E. P., & Jenkins,G. M. (1970). Time series
analysis forecasting and control.
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Published
2020-06-30
How to Cite
Velos, S. P., Go, M. B., Bate, G. P., & Joyohoy, E. B. (2020). A Seasonal Autoregressive Integrated Moving Average (SARIMA) Model to Forecasting Tourist Arrival in the Philippines: A Case Study in Moalboal, Cebu (Philippines). Recoletos Multidisciplinary Research Journal, 8(1), 67–78. https://doi.org/10.32871/rmrj2008.01.05
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