MATHEMATICAL MODELING OF PASSENGER FLOW AT TOCUMEN INTERNATIONAL AIRPORT USING SARIMA TIME SERIES ANALYSIS
pdf (Español (España))
FLIP
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Esta obra está bajo licencia internacional https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
La revista (y sus contenidos) emplean las licencias Creative Commons, específicamente la del tipo CC BY NC SA 4.0, la cual establece que “el beneficiario de la licencia tiene el derecho de copiar, distribuir, exhibir y representar la obra y hacer obras derivadas siempre y cuando reconozca y cite la obra de la forma especificada por el autor o el licenciante”. La licencia del tipo CC BY NC SA 4.0 contempla tres categorías,
- Atribución.
- No Comercialización de la obra.
- Compartir igual
Los lectores son libres de:
- Compartir — copiar y redistribuir el material en cualquier medio o formato
- Adaptar — remezclar, transformar y construir a partir del materialLa licenciante no puede revocar estas libertades en tanto usted siga los términos de la licencia
- Siempre y cuando se respeten y contemplen la atribución de autoría y la no comercialización del material.
Abstract
Time series are essential in various fields, allowing for pattern analysis and future trend prediction. The ARIMA model, while popular, assumes stationarity, which may not be suitable for series with seasonal patterns. Hence, the SARIMA model is introduced, which considers seasonality. Tocumen International Airport in Panama, a significant hub in Latin America, needs to predict passenger flow for efficient management. This study adopts a non-experimental and longitudinal approach to analyze and predict passenger flow using SARIMA. Despite data challenges from 2020 and 2021 due to the pandemic, the model provided accurate predictions. Efficient airport management requires foreseeing future trends, and tools like SARIMA are valuable in this context. However, the proper selection of parameters and validation is crucial. In the tourism realm, predicting trends and adapting to changes is essential for the sector's sustainability.
References
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: forecasting and control. Holden-Day.
Brockwell, P. J., & Davis, R. A. (2002). Introduction to time series and forecasting. Springer.
Chen, Q., Zhao, H., Qiu, H., Wang, Q., Zeng, D., & Ye, M. (2022). Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models. Journal of Infection in Developing Countries.
Chi, Y. (2022). Time Series Modeling and Forecasting of Monthly Mean Sea Level (1978 – 2020): SARIMA and Multilayer Perceptron Neural Network. International Journal of Data Science.
Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Elsevier.
Velu, S., Ravi, V., & Tabianan, K. (2022). Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases. Health Information Science and Systems.
Wu, B., Wang, L., Tao, R., & Zeng, Y. (2022). Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19. Neural Computing and Applications.
Downloads
Cited by
