Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia
The airline sector is increasingly using predictive models based on historical demand data towards making decisions in intermediate and long term. This study estimates three different models using data from all the Colombian territory regarding national and international flights like the GDP, employ...
- Autores:
-
Arteta Heins, Alonsa
Montes Rodriguez, Karol
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13252
- Acceso en línea:
- https://hdl.handle.net/11323/13252
https://repositorio.cuc.edu.co
- Palabra clave:
- Predictive models
Aviation sector
Flight demand
GDP
Employment rates
SARIMA
SARIMAX
Performance
PIB
Colombia
Modelos predictivos
Sector aéreo
Demanda de vuelos
Tasas de empleo
Parámetros
- Rights
- closedAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
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|
| dc.title.spa.fl_str_mv |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| title |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| spellingShingle |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia Predictive models Aviation sector Flight demand GDP Employment rates SARIMA SARIMAX Performance PIB Colombia Modelos predictivos Sector aéreo Demanda de vuelos Tasas de empleo Parámetros |
| title_short |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| title_full |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| title_fullStr |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| title_full_unstemmed |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| title_sort |
Pronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de Colombia |
| dc.creator.fl_str_mv |
Arteta Heins, Alonsa Montes Rodriguez, Karol |
| dc.contributor.advisor.none.fl_str_mv |
Figueroa Loaiza, Miguel A. borrero restrepo, diego |
| dc.contributor.author.none.fl_str_mv |
Arteta Heins, Alonsa Montes Rodriguez, Karol |
| dc.subject.proposal.eng.fl_str_mv |
Predictive models Aviation sector Flight demand GDP Employment rates SARIMA SARIMAX Performance PIB |
| topic |
Predictive models Aviation sector Flight demand GDP Employment rates SARIMA SARIMAX Performance PIB Colombia Modelos predictivos Sector aéreo Demanda de vuelos Tasas de empleo Parámetros |
| dc.subject.proposal.spa.fl_str_mv |
Colombia Modelos predictivos Sector aéreo Demanda de vuelos Tasas de empleo Parámetros |
| description |
The airline sector is increasingly using predictive models based on historical demand data towards making decisions in intermediate and long term. This study estimates three different models using data from all the Colombian territory regarding national and international flights like the GDP, employment rates, among others socioeconomics variables. Model structures considered include Holt-Winters and variations of time series models (SARIMA and SARIMAX). For comparing the forecasting performance parameters such as SSE, ESS, RMSE, MAE and MAPE were used. The analysis shows significant gains in model fit when GDP, employment rate and tourism factors were included in the SARIMAX model for national flight predictions, and as for national predictions the SARIMA model had the best performance in terms of the mean absolute percentage error, MAPE. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-08-05T13:10:03Z |
| dc.date.available.none.fl_str_mv |
2024-08-05T13:10:03Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/13252 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co |
| url |
https://hdl.handle.net/11323/13252 https://repositorio.cuc.edu.co |
| identifier_str_mv |
Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Adrangi, B., Chatrath, A., Raffiee, K., 2001. The demand for US air transport service: a chaos and nonlinearity investigation. Transportation Research Part E: Logistics and Transportation Review 37, 337–353. https://doi.org/10.1016/S1366-5545(00)00017-X Aeronáutica Civil, 2017. La Aviación En Cifras. Albers, S., Klapper, D., Konradt, U., Walter, A., Wolf, J. (Eds.), 2007. Methodik der empirischen Forschung. Gabler, Wiesbaden. https://doi.org/10.1007/978-3-8349-9121-8 Aloulou, M., Haouari, M., Zeghal Mansour, F., 2013. A model for enhancing robustness of aircraft and passenger connections. Transportation Research Part C: Emerging Technologies 32, 48–60. https://doi.org/10.1016/j.trc.2013.03.008 Baker, D., Merkert, R., & Kamruzzaman, M. (2015). Regional aviation and economic growth: cointegration and causality analysis in Australia. Journal of Transport Geography, 43, 140-150. Box, G.E.P., Jenkins, G.M., 1976. Time series analysis: forecasting and control. Holden-Day, San Francisco. Button, K., & Taylor, S. (2000). International air transportation and economic development. Journal of Air Transport Management, 6(4), 209-222. Carson, R.T., Cenesizoglu, T., Parker, R., 2010. Forecasting (aggregate) demand for US commercial air travel. International Journal of Forecasting 19. Chen, C.-F., Chang, Y.-H., Chang, Y.-W., 2009. Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan. Transportmetrica 5, 125–140. https://doi.org/10.1080/18128600802591210 Coldren, G.M., Koppelman, F.S., 2005. Modeling the competition among air-travel itinerary shares: GEV model development. Transportation Research Part A: Policy and Practice 39. De Neufville, R. (Ed.), 2013. Airport systems: planning, design, and management, 2nd ed. ed. McGraw-Hill, New York. Dobruszkes, F., Mondou, V., 2013. Aviation liberalization as a means to promote international tourism: The EU–Morocco case. Journal of Air Transport Management 29, 23–34. https://doi.org/10.1016/j.jairtraman.2013.02.001 Faraway, J.J., Chatfield, C., 1998. Time series forecasting with neural networks: a comparative study using the air line data. Ferbar, L., Mojškerc, B., Toman, A., 2016. Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics 181. https://doi.org/10.1016/j.ijpe.2016.08.004 Flyvbjerg, B., Skamris Holm, M.K., Buhl, S.L., 2005. How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation. Journal of the American Planning Association 71, 131–146. https://doi.org/10.1080/01944360508976688 Forsyth, P. (2006). Estimating the marginal social cost of airport noise and congestion. Transportation Research Part D: Transport and Environment, 11(5), 344-356. Gelhausen, M., Berster, P., Wilken, D., 2018. A new direct demand model of long-term forecasting air passengers and air transport movements at German airports. Journal of Air Transport Management 71. https://doi.org/10.1016/j.jairtraman.2018.04.001 Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499- 510. Graham, A., Papatheodorou, A., & Forsyth, P. (2008). Aviation and Tourism. IATA, 2019. El valor de la aviacion en Colombia. Jones, A.R., 2018. Best Fit Lines & Curves: And Some Mathe-Magical Transformations. Routledge. Kamel, M. A., & Shehata, E. H. (2015). "A comparison between SARIMA and SARIMAX models for forecasting the volume of demand for the Egyptian tourism industry." International Journal of Business and Management, 10(4), 182-189. Kays, H.M.E., Karim, A.N.M., Daud, M.R.C., Varela, M.L.R., Putnik, G.D., Machado, J.M., 2018. A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component. Applied Sciences 8, 530. https://doi.org/10.3390/app8040530 Lai, S.L., Lu, W.-L., 2005. Impact analysis of September 11 on air travel demand in the USA. Journal of Air Transport Management, Journal of Air Transport Management. - Elsevier, ISSN 0969-6997. - Vol. 11.2005, 6, p. 455-458 11. Lai, S.L., Lu, W.-L., 2005. Impact analysis of September 11 on air travel demand in the USA. Journal of Air Transport Management, Journal of Air Transport Management. - Elsevier, ISSN 0969-6997. - Vol. 11.2005, 6, p. 455-458 11. Lian, J., Denstadli, J., 2010. Booming Leisure Air Travel to Norway - The Role of Airline Competition. Scandinavian Journal of Hospitality and Tourism - SCAND J HOSP TOUR 10, 1–15. https://doi.org/10.1080/15022250.2010.484215 Montesino Pouzols, F., Lopez, D., Barros, A., 2011. Mining and Control of Network Traffic by Computational Intelligence 342. https://doi.org/10.1007/978-3-642-18084-2 Nai, W., Liu, L., Wang, S., Dong, D., 2017. An EMD-SARIMA-based modeling approach for air traffic forecasting. Algorithms 10, 139. https://doi.org/10.3390/a10040139 Nicolaisen, M.S., Driscoll, P.A., 2014. Ex-Post Evaluations of Demand Forecast Accuracy: A Literature Review. Transport Reviews 34, 540–557. https://doi.org/10.1080/01441647.2014.926428 Nieto, M., Carmona Benitez, R., 2018. ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management 71. https://doi.org/10.1016/j.jairtraman.2018.05.007 Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. John Wiley & Sons. Saâdaoui, F., Saadaoui, H., Rabbouch, H., 2020. Hybrid Feedforward ANN with NLS-Based Regression Curve Fitting for US Air Traffic Forecasting. Neural Computing and Applications 32, 10073–10085. https://doi.org/10.1007/s00521-019-04539-5 Scarpel, R.A., 2013. Forecasting air passengers at São Paulo International Airport using a mixture of local experts model. Journal of air transport management, Journal of air transport management. - Amsterdam [u.a.] : Elsevier, ISSN 0969-6997, ZDB-ID 1208154-1. - Vol. 26.2013, p. 35-39 26. Sivrikaya, O., Tunc, E., 2013. Demand Forecasting for Domestic Air Transportation in Turkey. TOTJ 7, 20–26. https://doi.org/10.2174/1874447820130508001 Srisaeng, P., Baxter, G., Wild, G., 2015. An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low cost carrier passenger demand. Aviation 19, 150– 163. https://doi.org/10.3846/16487788.2015.1104806 Standford University, n.d. Error Sum of Squares [WWW Document]. URL https://hlab.stanford.edu/brian/error_sum_of_squares.html (accessed 11.6.21). Tsui, W. H. K., Balli, H. O., Gilbey, A., & Gow, H. (2014). Operational efficiency of Asia– Pacific airports. Journal of Air Transport Management, 40, 16-24. US Congress Joint Economic Committee, 2008. Your flight has been delayed again : flight delays cost passengers, airlines, and the U.S. economy billions-. Valdes, V., 2015. Determinants of air travel demand in Middle Income Countries. Journal of Air Transport Management 42, 75–84. https://doi.org/10.1016/j.jairtraman.2014.09.002 Wadud, Z., 2013. Simultaneous Modeling of Passenger and Cargo Demand at an Airport. Transportation Research Record Journal of the Transportation Research Board 2336, 63– 74. https://doi.org/10.3141/2336-08 Wang, S., Feng, J., Liu, G., 2013. Application of seasonal time series model in the precipitation forecast. Mathematical and Computer Modelling 58, 677–683. https://doi.org/10.1016/j.mcm.2011.10.034 Warnock-Smith, D., Morrell, P., 2008. Air transport liberalisation and traffic growth in tourism dependent economies: A case-history of some US-Caribbean markets. Journal of Air Transport Management 14, 82–91. https://doi.org/10.1016/j.jairtraman.2008.02.001 Wijnen, R.A.A., Walker, W.E., Kwakkel, J.H., 2008. Decision Support for Airport Strategic Planning. Transportation Planning and Technology 31, 11–34. https://doi.org/10.1080/03081060701835670 Xie, G., Wang, S., Lai, K.K., 2014. Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air Transport Management 37, 20–26. https://doi.org/10.1016/j.jairtraman.2014.01.009 Xu, S., Chan, H., Zhang, T., 2018. Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach. Transportation Research Part E Logistics and Transportation Review 122, 169–180. https://doi.org/10.1016/j.tre.2018.12.005 Zhang, F., Graham, D.J., 2020. Air transport and economic growth: a review of the impact mechanism and causal relationships. Transport Reviews 40, 506–528. https://doi.org/10.1080/01441647.2020.1738587 |
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Civil y Ambiental |
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Barranquilla, Colombia |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbFigueroa Loaiza, Miguel A.borrero restrepo, diegoArteta Heins, AlonsaMontes Rodriguez, Karol2024-08-05T13:10:03Z2024-08-05T13:10:03Z2024https://hdl.handle.net/11323/13252Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.coThe airline sector is increasingly using predictive models based on historical demand data towards making decisions in intermediate and long term. This study estimates three different models using data from all the Colombian territory regarding national and international flights like the GDP, employment rates, among others socioeconomics variables. Model structures considered include Holt-Winters and variations of time series models (SARIMA and SARIMAX). For comparing the forecasting performance parameters such as SSE, ESS, RMSE, MAE and MAPE were used. The analysis shows significant gains in model fit when GDP, employment rate and tourism factors were included in the SARIMAX model for national flight predictions, and as for national predictions the SARIMA model had the best performance in terms of the mean absolute percentage error, MAPE.El sector aéreo utiliza cada vez más modelos predictivos basados en datos históricos de demanda para la toma de decisiones a medio y largo plazo. Este estudio estima tres modelos diferentes utilizando datos de todo el territorio colombiano sobre vuelos nacionales e internacionales como el PIB, tasas de empleo, entre otras variables socioeconómicas. Las estructuras de modelos consideradas incluyen Holt-Winters y variaciones de modelos de series temporales (SARIMA y SARIMAX). Para comparar el desempeño del pronóstico se utilizaron parámetros como SSE, ESS, RMSE, MAE y MAPE. El análisis muestra mejoras significativas en el ajuste del modelo cuando se incluyeron el PIB, la tasa de empleo y los factores turísticos en el modelo SARIMAX para las predicciones de vuelos nacionales, y en cuanto a las predicciones nacionales, el modelo SARIMA tuvo el mejor desempeño en términos del error porcentual absoluto medio, MAPE.Introducción 10 – Justificación 14 – Objetivos 16 – General 16 – Específicos 16 -- Estado del arte 17 – Metodología 23 -- Series de tiempo 24 -- SARIMA 24 -- SARIMAX 25 -- Holt Winters Model 26 – Desempeño 27 – Datos 29 -- Información considerada 29 – Resultados 34 – Discusiones 40 – Conclusiones 42 -- Estudios futuros 44 -- PRONÓSTICO DE LA DEMANDA DE PASAJEROS DE TRANSPORTE AÉREO 5 Referencias 45 --Ingeniero(a) CivilPregrado50 páginasapplication/pdfspaCorporación Universidad de la CostaCivil y AmbientalBarranquilla, ColombiaIngeniería CivilPronóstico de la demanda de pasajeros de transporte aéreo: un estudio de caso de ColombiaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionColombiaAdrangi, B., Chatrath, A., Raffiee, K., 2001. The demand for US air transport service: a chaos and nonlinearity investigation. Transportation Research Part E: Logistics and Transportation Review 37, 337–353. https://doi.org/10.1016/S1366-5545(00)00017-XAeronáutica Civil, 2017. La Aviación En Cifras.Albers, S., Klapper, D., Konradt, U., Walter, A., Wolf, J. (Eds.), 2007. Methodik der empirischen Forschung. Gabler, Wiesbaden. https://doi.org/10.1007/978-3-8349-9121-8Aloulou, M., Haouari, M., Zeghal Mansour, F., 2013. A model for enhancing robustness of aircraft and passenger connections. Transportation Research Part C: Emerging Technologies 32, 48–60. https://doi.org/10.1016/j.trc.2013.03.008Baker, D., Merkert, R., & Kamruzzaman, M. (2015). Regional aviation and economic growth: cointegration and causality analysis in Australia. Journal of Transport Geography, 43, 140-150.Box, G.E.P., Jenkins, G.M., 1976. Time series analysis: forecasting and control. Holden-Day, San Francisco.Button, K., & Taylor, S. (2000). International air transportation and economic development. Journal of Air Transport Management, 6(4), 209-222.Carson, R.T., Cenesizoglu, T., Parker, R., 2010. Forecasting (aggregate) demand for US commercial air travel. International Journal of Forecasting 19.Chen, C.-F., Chang, Y.-H., Chang, Y.-W., 2009. Seasonal ARIMA forecasting of inbound air travel arrivals to Taiwan. Transportmetrica 5, 125–140. https://doi.org/10.1080/18128600802591210Coldren, G.M., Koppelman, F.S., 2005. Modeling the competition among air-travel itinerary shares: GEV model development. Transportation Research Part A: Policy and Practice 39.De Neufville, R. (Ed.), 2013. Airport systems: planning, design, and management, 2nd ed. ed. McGraw-Hill, New York.Dobruszkes, F., Mondou, V., 2013. Aviation liberalization as a means to promote international tourism: The EU–Morocco case. Journal of Air Transport Management 29, 23–34. https://doi.org/10.1016/j.jairtraman.2013.02.001Faraway, J.J., Chatfield, C., 1998. Time series forecasting with neural networks: a comparative study using the air line data.Ferbar, L., Mojškerc, B., Toman, A., 2016. Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics 181. https://doi.org/10.1016/j.ijpe.2016.08.004Flyvbjerg, B., Skamris Holm, M.K., Buhl, S.L., 2005. How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation. Journal of the American Planning Association 71, 131–146. https://doi.org/10.1080/01944360508976688Forsyth, P. (2006). Estimating the marginal social cost of airport noise and congestion. Transportation Research Part D: Transport and Environment, 11(5), 344-356.Gelhausen, M., Berster, P., Wilken, D., 2018. A new direct demand model of long-term forecasting air passengers and air transport movements at German airports. Journal of Air Transport Management 71. https://doi.org/10.1016/j.jairtraman.2018.04.001Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499- 510.Graham, A., Papatheodorou, A., & Forsyth, P. (2008). Aviation and Tourism. IATA, 2019. El valor de la aviacion en Colombia.Jones, A.R., 2018. Best Fit Lines & Curves: And Some Mathe-Magical Transformations. Routledge.Kamel, M. A., & Shehata, E. H. (2015). "A comparison between SARIMA and SARIMAX models for forecasting the volume of demand for the Egyptian tourism industry." International Journal of Business and Management, 10(4), 182-189.Kays, H.M.E., Karim, A.N.M., Daud, M.R.C., Varela, M.L.R., Putnik, G.D., Machado, J.M., 2018. A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component. Applied Sciences 8, 530. https://doi.org/10.3390/app8040530Lai, S.L., Lu, W.-L., 2005. Impact analysis of September 11 on air travel demand in the USA. Journal of Air Transport Management, Journal of Air Transport Management. - Elsevier, ISSN 0969-6997. - Vol. 11.2005, 6, p. 455-458 11.Lai, S.L., Lu, W.-L., 2005. Impact analysis of September 11 on air travel demand in the USA. Journal of Air Transport Management, Journal of Air Transport Management. - Elsevier, ISSN 0969-6997. - Vol. 11.2005, 6, p. 455-458 11.Lian, J., Denstadli, J., 2010. Booming Leisure Air Travel to Norway - The Role of Airline Competition. Scandinavian Journal of Hospitality and Tourism - SCAND J HOSP TOUR 10, 1–15. https://doi.org/10.1080/15022250.2010.484215Montesino Pouzols, F., Lopez, D., Barros, A., 2011. Mining and Control of Network Traffic by Computational Intelligence 342. https://doi.org/10.1007/978-3-642-18084-2Nai, W., Liu, L., Wang, S., Dong, D., 2017. An EMD-SARIMA-based modeling approach for air traffic forecasting. Algorithms 10, 139. https://doi.org/10.3390/a10040139Nicolaisen, M.S., Driscoll, P.A., 2014. Ex-Post Evaluations of Demand Forecast Accuracy: A Literature Review. Transport Reviews 34, 540–557. https://doi.org/10.1080/01441647.2014.926428Nieto, M., Carmona Benitez, R., 2018. ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management 71. https://doi.org/10.1016/j.jairtraman.2018.05.007Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. John Wiley & Sons.Saâdaoui, F., Saadaoui, H., Rabbouch, H., 2020. Hybrid Feedforward ANN with NLS-Based Regression Curve Fitting for US Air Traffic Forecasting. Neural Computing and Applications 32, 10073–10085. https://doi.org/10.1007/s00521-019-04539-5Scarpel, R.A., 2013. Forecasting air passengers at São Paulo International Airport using a mixture of local experts model. Journal of air transport management, Journal of air transport management. - Amsterdam [u.a.] : Elsevier, ISSN 0969-6997, ZDB-ID 1208154-1. - Vol. 26.2013, p. 35-39 26.Sivrikaya, O., Tunc, E., 2013. Demand Forecasting for Domestic Air Transportation in Turkey. TOTJ 7, 20–26. https://doi.org/10.2174/1874447820130508001Srisaeng, P., Baxter, G., Wild, G., 2015. An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low cost carrier passenger demand. Aviation 19, 150– 163. https://doi.org/10.3846/16487788.2015.1104806Standford University, n.d. Error Sum of Squares [WWW Document]. URL https://hlab.stanford.edu/brian/error_sum_of_squares.html (accessed 11.6.21).Tsui, W. H. K., Balli, H. O., Gilbey, A., & Gow, H. (2014). Operational efficiency of Asia– Pacific airports. Journal of Air Transport Management, 40, 16-24.US Congress Joint Economic Committee, 2008. 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Transport Reviews 40, 506–528. https://doi.org/10.1080/01441647.2020.1738587Predictive modelsAviation sectorFlight demandGDPEmployment ratesSARIMASARIMAXPerformancePIBColombiaModelos predictivosSector aéreoDemanda de vuelosTasas de empleoParámetrosPublicationORIGINALPronóstico de la demanda de pasajeros de transporte aéreo.pdfPronóstico de la demanda de pasajeros de transporte aéreo.pdfTesisapplication/pdf752641https://repositorio.cuc.edu.co/bitstreams/590a5cb3-019f-4ca7-83a6-2aaa2bddb964/download83ad288a0f5c4fe783878212ffce4b57MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/4fe5d6a0-9133-4f13-aa1d-0cad98459060/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTPronóstico de la demanda de pasajeros de transporte aéreo.pdf.txtPronóstico de la demanda de pasajeros de transporte aéreo.pdf.txtExtracted 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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