رسالة جامعية
Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia ; Evaluation of time series models to forecast air transportation demand in the short and medium term in Colombia
| العنوان: | Evaluación de modelos de series de tiempo para pronosticar la demanda de transporte aéreo a corto y mediano plazo en Colombia ; Evaluation of time series models to forecast air transportation demand in the short and medium term in Colombia |
|---|---|
| المؤلفون: | Amezquita Bravo, Cristian Camilo |
| المساهمون: | Moreno Rivas, Álvaro Martin |
| بيانات النشر: | Universidad Nacional de Colombia Bogotá - Ciencias Económicas - Maestría en Ciencias Económicas Escuela de Economía Facultad de Ciencias Económicas Bogotá, Colombia Universidad Nacional de Colombia - Sede Bogotá |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 330 - Economía, Time-series analysis, Aeronautics, commercial, Forecasting techniques, Análisis de series de tiempo, Aviación comercial, Técnicas de predicción, Pronóstico de demanda, Transporte aéreo, Series de tiempo, ARIMA, SARIMA, ARIMAX, Forecasting demand, Air transport, Air passengers demand, Time series |
| جغرافية الموضوع: | Colombia |
| الوصف: | ilustraciones, gráficas, tablas ; El presente trabajo suministra una evaluación de la capacidad predictiva de diferentes modelos de serie de tiempo en datos mensuales del transporte aéreo de pasajeros de tráfico nacional, internacional y total entre 1994 y el 2019. Los modelos estimados son: modelo de regresión armónica, modelo de suavizado exponencial de Holt-Winters, modelo autorregresivo integrado de media móvil (ARIMA), ARIMA estacional (SARIMA) y ARIMA con variable exógena (ARIMAX). Los resultados muestran que los modelos SARIMA y SARIMAX proveen los mejores resultados en cuanto a bondad de ajuste y precisión con pronósticos en términos de MAPE y RMSE por debajo del umbral del 3% de la realización puntual media. El modelo multivariado SARIMAX supera los resultados de pronóstico de los modelos univariantes. El PIB logra potenciar los resultados del modelo y se confirma la relación positiva que posee con el sector aéreo. Se evaluaron otras variables como los precios del petróleo y choques exógenos locales e internacionales pero su efecto resultó ser no significativo. El modelo de regresión armónica solo puede predecir con alta precisión los pasajeros de tráfico internacional mientras que el modelo de Holt Winters logra obtener previsiones altamente precisas para la serie de tráfico internacional y total. (Texto tomado de la fuente). ; This thesis provides an evaluation of the predictive capacity of different time series models in monthly data of the air transport of passengers of national, international, and total traffic between 1994 and 2019. The estimated models are harmonic regression model, Holt-Winters exponential smoothing model, integrated moving average autoregressive model (ARIMA), seasonal ARIMA (SARIMA) and ARIMA with exogenous variable (ARIMAX). The results show that the SARIMA and SARIMAX models provide the best results in terms of goodness of fit and precision with forecasts in terms of MAPE and RMSE below the threshold of 3% of the average punctual realization. The SARIMAX multivariate model ... |
| نوع الوثيقة: | master thesis |
| وصف الملف: | x, 46 páginas; application/pdf |
| اللغة: | Spanish; Castilian |
| العلاقة: | Aeronáutica Civil, (2014a). Guía para la elaboración de Planes Maestros Aeroportuarios – PMA, Circular Reglamentaria N° 053, https://www.aerocivil.gov.co/normatividad/CIRCULARES%20AGA/CI%20053%20-%20V2.pdfTest; Aeronáutica Civil, (2014b). Actualización del plan maestro del Aeropuerto Internacional ElDorado reporte final, Tylin international, https://www.aerocivil.gov.co/aeropuertos/_layouts/15/WopiFrame,aspx?sourcedoc=/aeropuertos/Consecionados/El%20Dorado%20-%20Bogot%C3%A1,pdf&action=defaultTest; Aeronáutica Civil, (2019). Aeropuertos – Planes Maestro, https://www.aerocivil.gov.co/aeropuertos/planes%20maestros/forms/allitems.aspxTest; Alhassan, R., Abdulaal, R., & Alsulami, H. (2017). A Forecasting Model for Satisfying the Demand of International Flight Passengers Having Domestic Flight Connection. Journal of King Abdulaziz University Engineering Sciences, 28(1), 49–63. https://doi.org/10.4197/eng.28-1.4Test; Andreoni, A., & Postorino, M. N. (2006). a Multivariate Arima Model To Forecast.; Artis, M. J., Clavel, J. G., Hoffmann, M., & Nachane, D. M. (2007). Harmonic Regression Models: A Comparative Review with Applications. SSRN Electronic Journal, 333. https://doi.org/10.2139/ssrn.1017519Test; Bloomfield, P. (2000). Fourier Analysis of Time Series: An Introduction. In Journal of the American Statistical Association (Vol. 95, Issue 452, p. 1373). https://doi.org/10.2307/2669794Test; Chatfield, C. (2000). Time-Series Forecasting. In Urologiia (Moscow, Russia : 1999) (Issue 1). Chapman & Hall/CRC. http://www.ncbi.nlm.nih.gov/pubmed/16856455Test; Chen, C. F., Chang, Y. H., & Chang, Y. W. (2009). Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan. Transportmetrica, 5(2), 125–140. https://doi.org/10.1080/18128600802591210Test; Chow, G. C., & Lin, A. (1971). Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series. The Review of Economics and Statistics, 53(4), 372. https://doi.org/10.2307/1928739Test; Coshall, J. (2006). Time series analyses of UK outbound travel by air. Journal of Travel Research, 44(3), 335–347. https://doi.org/10.1177/0047287505279003Test; Cowpertwait, P., & Metcalfe, A. (2009). Introductory Time Series with R. Springer. http://www.springer.com/la/book/9780387886978%0Ahttp://www.springer.com/la/book/9780387886978?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBookTest; Dantas, T. M., Cyrino Oliveira, F. L., & Varela Repolho, H. M. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116–123. https://doi.org/10.1016/j.jairtraman.2016.12.006Test; Denton, F. T. (1971). Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization. Journal of the American Statistical Association, 66(333), 99–102. https://doi.org/10.1080/01621459.1971.10482227Test; Díaz Olariaga, O., Girón Amaya, E., & Mora-Camino, F. (2017, October). Pronóstico de la demanda de pasajeros en aeropuertos privatizados. In VI Congreso Internacional de la Red Iberoamericana de Investigación en Transporte Aéreo (pp. 10-12). https://www.researchgate.net/publication/320434160_PRONOSTICO_DE_LA_DEMANDA_DE_PASAJEROS_EN_AEROPUERTOS_PRIVATIZADOSTest; Díaz Olariaga, Ó. (2016). Análisis del desarrollo reciente del transporte aéreo en Colombia. Revista Transporte y Territorio, 0(14), 122–143. https://doi.org/10.34096/rtt.i14.2432Test; 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/S0169-2070Test(02)00057-2; Emiray, E.; Rodríguez, G. (2003). Evaluating time series models in short and long-term forecasting of Canadian air passenger data. No. 0306E, January 2003.; Franses, P. H. (1990). TESTING FOR SEASONAL UNIT ROOTS IN MONTHLY DATA. Erasmus University Rotterdam.; Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499–510. https://doi.org/10.1016/S0261-5177Test(02)00009-2; Granger, C. W. J., & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics, 2, 111–120. https://doi.org/10.1002/9780470996249.ch27Test; Greene, W. H. (1999). Análisis Econométrico (3rd ed.). Prentice Hall.; Gujarati, D., & Porter, D. (2010). Econometría (Quinta edi). McGraw Hill.; Hamilton, J. (1994). Time Series Analysis. Princeton University Press.; Harvey, A. C. (1993). Time Series Models. In Angewandte Chemie International Edition (2nd Editio, Vol. 6, Issue 11). MIT Press.; Hylleberg, S., Engle, R. F., Granger, C. W. J., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. https://doi.org/10.1016/0304-4076Test(90)90080-D; IATA. (2007). Aviation Economic Benefits. IATA Economics Briefing, N° 8.; IATA. (2020). Air connectivity: Measuring the connections that drive economic growth. 57(2), Contents2–Contents2. https://doi.org/10.3143/geriatrics.57.contents2Test; Kim, J. H., & Moosa, I. (2001). Seasonal behaviour of monthly international tourist flows: Specification and implications for forecasting models. Tourism Economics, 7(4), 381–396. https://doi.org/10.5367/000000001101297937Test; Lim, C., & Mcaleer, M. (2010). A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32(4), 499–509. https://doi.org/10.1080/000368400322660Test; Lim, C., & McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4), 965–977. https://doi.org/10.1016/S0160-7383Test(01)00006-8; 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). https://doi.org/10.5430/ijba.v2n2p14Test; Lutkepohl, H., & Kratzig, M. (2013). Applied Time Series Econometrics Time. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). Cambridge University Press.; Marazzo, M., Scherre, R., & Fernandes, E. (2010). Air transport demand and economic growth in Brazil: A time series analysis. Transportation Research Part E: Logistics and Transportation Review, 46(2), 261–269. https://doi.org/10.1016/j.tre.2009.08.008Test; Martínez-Ortíz, A., & García-Romero, H. (2016). Competitividad en el transporte aéreo en Colombia. 218. https://www.repository.fedesarrollo.org.co/bitstream/handle/11445/3280/Repor_Junio_2016_Martinez_y_Garcia.pdf?sequence=2&isAllowed=y&fbclid=IwAR2zdrF15WDXB7QHagHNepZLhhwPxF5nAE-06gXbchtCeFt6oq1I8ChgYGo%0Ahttp://www.repository.fedesarrollo.org.co/handle/11Test; Mogollón, J. D. (2020). Pronóstico de la demanda del transporte aéreo en aeropuerto distribuidor. Aplicación al caso de Aeropuerto Internacional el Dorado. 15, 1–137.; Nieto, M. R., & Carmona-Benítez, R. B. (2018). ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management, 71(June), 1–8. https://doi.org/10.1016/j.jairtraman.2018.05.007Test; Oh, C. O., & Morzuch, B. J. (2005). Evaluating time-series models to forecast the demand for tourism in Singapore: Comparing within-sample and postsample results. Journal of Travel Research, 43(4), 404–413. https://doi.org/10.1177/0047287505274653Test; Olivera, M., Cabrera, P., Bermúdez, W., & Hernandez, A. (2011). El impacto del transporte aéreo en la economía colombiana y las políticas públicas. In Cuaderno de fedesarrollo N.34. www.atac.aero/imagenesindex/cuaderno_fedesarrollo_34.pdf; Ramos, I., & Cardenas, L. (2016). Prognosis de tráfico en el Aeropuerto de Bogotá-El Dorado y análisis de la relación demanda-capacidad.; Sharif Azadeh, S., Labib, R., & Savard, G. (2013). Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management, 7(1), 18–36. https://doi.org/10.1504/IJRM.2013.053358Test; Straszheim, M. (1978). Airline demand functions in the North Atlantic and their pricing implications. Journal of Transport Economics and Policy, 12(2), 179–195.; The R Development Core Team. (2008). R: A Language and Environment for Statistical Computing: Vol. Version 2. https://www.r-project.orgTest/; Tsui, W. H. K., Ozer Balli, H., Gilbey, A., & Gow, H. (2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42(2014), 62–76. https://doi.org/10.1016/j.tourman.2013.10.008Test; United States Bureau Census. (2021). X-13ARIMA-SEATS Reference Manual Accessible HTML Output Version.; Wei, W. (2006). Time Series Analysis: Univariate and Multivariate Methods. In Technometrics (Vol. 33, Issue 1). Pearson. https://doi.org/10.1080/00401706.1991.10484777Test; Williams, B. M. (2001). Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Transportation Research Record, 01, 194–200.; Yule, G. U. (1926). Why do we Sometimes get Nonsense-Correlations between Time-Series?: A Study in Sampling and the Nature of Time-Series. Journal of the Royal Statistical Society, 89(1), 1–63.; https://repositorio.unal.edu.co/handle/unal/81124Test; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.coTest/ |
| الإتاحة: | https://doi.org/10.2307/2669794Test https://doi.org/10.1177/0047287505279003Test https://doi.org/10.1080/01621459.1971.10482227Test https://doi.org/10.34096/rtt.i14.2432Test https://doi.org/10.3143/geriatrics.57.contents2Test https://doi.org/10.1080/00401706.1991.10484777Test https://repositorio.unal.edu.co/handle/unal/81124Test https://repositorio.unal.edu.coTest/ |
| حقوق: | Atribución-NoComercial-SinDerivadas 4.0 Internacional ; http://creativecommons.org/licenses/by-nc-nd/4.0Test/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.3E2CFB94 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |