Combination forecasting method using Bayesian models and a metaheuristic, case study
Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combinati...
- Autores:
-
Higuita Alzate, David
Valencia Cárdenas, Marisol
Correa Morales, Juan Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2018
- Institución:
- Tecnológico de Antioquia
- Repositorio:
- Repositorio Tdea
- Idioma:
- spa
- OAI Identifier:
- oai:dspace.tdea.edu.co:tdea/2843
- Acceso en línea:
- https://dspace.tdea.edu.co/handle/tdea/2843
- Palabra clave:
- Forecasts
Productos perecederos
Perishable products
Produto perecível
Produit périssable
Statistics and probability
Estadística y probabilidad
Pronósticos
Optimization theory
Teoría de optimización
Bayesian statistics
Estadística Bayesiana
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
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Combination forecasting method using Bayesian models and a metaheuristic, case study |
| dc.title.translated.none.fl_str_mv |
Método de combinación de pronósticos usando modelos Bayesianos y una metaheurística, caso de estudio |
| title |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| spellingShingle |
Combination forecasting method using Bayesian models and a metaheuristic, case study Forecasts Productos perecederos Perishable products Produto perecível Produit périssable Statistics and probability Estadística y probabilidad Pronósticos Optimization theory Teoría de optimización Bayesian statistics Estadística Bayesiana |
| title_short |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| title_full |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| title_fullStr |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| title_full_unstemmed |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| title_sort |
Combination forecasting method using Bayesian models and a metaheuristic, case study |
| dc.creator.fl_str_mv |
Higuita Alzate, David Valencia Cárdenas, Marisol Correa Morales, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Higuita Alzate, David Valencia Cárdenas, Marisol Correa Morales, Juan Carlos |
| dc.subject.other.none.fl_str_mv |
Forecasts |
| topic |
Forecasts Productos perecederos Perishable products Produto perecível Produit périssable Statistics and probability Estadística y probabilidad Pronósticos Optimization theory Teoría de optimización Bayesian statistics Estadística Bayesiana |
| dc.subject.agrovoc.none.fl_str_mv |
Productos perecederos Perishable products Produto perecível Produit périssable |
| dc.subject.proposal.none.fl_str_mv |
Statistics and probability Estadística y probabilidad Pronósticos Optimization theory Teoría de optimización Bayesian statistics Estadística Bayesiana |
| description |
Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector. Keywords: statistics and probability; forecasts; optimization theory; Bayesian statistics. |
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2018 |
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2018 |
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2023-05-01T19:20:02Z |
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2023-05-01T19:20:02Z |
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Artículo de revista |
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Doganis, P., Alexandridis, A., Patrinos P. and Sarimveis, H., Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J. Food Eng. 75(2), pp. 196-204, 2006. DOI:10.1016/j.jfoodeng.2005.03.056 Vidal, C., Londoño, J.C. y Contreras, F., Aplicación de modelos de inventarios en una cadena de abastecimiento de productos de consumo masivo con una bodega y N puntos de venta. Ing. y Compet. 6(1), pp. 35-52, 2004. Krajewski, L., Ritzman, L. and Malhotra, M., Administración de operaciones, 8th ed. 2008 Bowerman, B., Koehler, A. and O’Connell, R., Pronósticos, series de tiempo y regresión: un enfoque aplicado. México, DF. CENCAGE Learning, 2007 Congdon, P., Bayesian statistical modelling. London, England: Wiley Series in Probability and Statistics, 2002. Montoya, A., Montoya, I. y Castellanos, O., Situación de la competitividad de las Pyme en Colombia : elementos actuales y retos. Agron. Colomb. 28 (1), pp. 107-117, 2010. Valencia, M., Tróchez, J., Vanegas, J. y Restrepo, J., Modelo para el análisis de la quiebra financiera en Pymes agroindustriales antioqueñas. Apunt. del CENES. 35. Julio-Diciembre, pp. 147-168, 2016. Hanke, J. y Wichern, D., Pronósticos en los negocios. Prentice Hall. 2006 Valencia, M., Correa, J.C., Díaz, F. and Ramírez, S., Aplicación de modelación bayesiana y optimización para pronósticos de demanda. Ing. y Desarro. Univ. del Norte. 32(2), pp. 179-199, 2014. DOI: 10.14482/inde.32.2.5403 Fei, X., Lu, C.-C. and Liu, K., A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp. Res. Part C Emerg. Technol. 19(6), pp. 1306-1318, 2011. DOI: 10.1016/j.trc.2010.10.005 Sakauchi, T., Applying bayesian forecasting to predict new customers’ heating oil demand, 2011. West, B., Welch K. and Galecki, A., Linear mixed models: a practical guide using statistical software. 2006. DOI: 10.1080/15598608.2012.695708 Valencia, M., Estimación en modelos lineales mixtos con datos continuos usando transformaciones y distribuciones no normales. Tesis de Maestría. 2010. Hsiao, C. and S.K., Wan Is there an optimal forecast combination?. J. Econom. 178, PART 2, pp. 294-309, 2014. DOI: 10.1016/j.jeconom.2013.11.003 Chahkoutahi, F. and Khashei, M., A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy. 140, pp. 988-1004, 2017. DOI: 10.1016/j.energy.2017.09.009 Gao, W., Sarlak, V., Parsaei, M. R. and Ferdosi, M. Combination of fuzzy based on a meta-heuristic algorithm to predict electricity price in an electricity markets. Chem. Eng. Res. Des. 131, pp. 333-345, 2018. DOI: 10.1016/j.cherd.2017.09.021 Guerrero, L., Gómez, J.D., Zapata, D. and Valencia, M., Comparación de tres metaheurísticas para la optimización de inventarios con estimación de demanda. pp. 51-68, 2016. Makridakis, S., Hibon, M. and Moser, C., Accuracy of forecasting: an empirical investigation. J. R. Stat. Society. 142(2), pp. 97-145, 1979. DOI: 10.2307/2345077 Makridakis, S., Wheelwright, S. and McGee, V., Forecasting, methods and applications, Second Ed. John Wiley & Sons, 1983 Simchi-Levi, D., Kaminski, P. and Simchi-Levi, E., Designing and managing the supply chain, 3rd ed. New York: McGraw-Hill, 2008. Valencia, M., González, D. y Cardona, J., Metodología de un modelo de optimización para el pronóstico y manejo de inventarios usando el metaheurístico Tabú. Rev. Ing. 24(1), pp. 13-27, 2014. DOI: 10.15517/ring.v24i1.13771 Diebold, F.X. The past, present, and future of macroeconomic forecasting. J. Econ. Perspect. 12(2), pp. 175-192, 1998. Medina, S. y García, J., Predicción de demanda de energía en Colombia mediante un sistema de inferencia difuso neuronal. Energética. 33, pp. 15-24, 2005. DOI: 10.15446/energetica Keles, D., Scelle, J., Paraschiv, F. and Fichtner, W., Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy. 162, pp. 218-230, 2016 Claveria, O. and Torra, S., Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ. Model. 36, pp. 220-228, 2014. West, B., Welch, K. and Galecki, A., Linear mixed models: a practical guide using statistical software, first. 2006 Valencia, M., Estimación en modelos lineales mixtos con datos continuos usando transformaciones y distribuciones no normales. Tesis de grado. Universidad Nacional de Colombia, Sede Medellín. [en línea]. Disponible en: http://www.bdigital.unal.edu.co/1862/1/71680093.2010.pdf, 2010. Barbosa, C., Queiroz, C. and Migon, H., A dynamic linear model with extended skew-normal for the initial distribution of the state parameter. Comput. Stat. Data Anal. 74, pp. 64-80, 2014. DOI: 10.1016/j.csda.2013.12.008 Oracle., The Bayesian approach to forecasting. September, 2006 Kociecki, A., Kolasa, M. and Rubaszek, M., A Bayesian method of combining judgmental and model-based density forecasts. Econ. Model. 29, pp. 1349-1355, 2012. Gill, J., Bayesian methods for the social and behavioral sciences. Harvard University, 2007, pp. 9-11. Valencia-Cárdenas, M., Dynamic model for the multiproduct inventory optimization with multivariate demand. Tesis de Doctorado. Universidad Nacional de Colombia, Sede Medellín, 2016 Zellner, A., An introduction to Bayesian inference in econometrics, Second. Wiley Classics Library, 1996 Story, R.E., An explanation of the effectiveness of latent semantic indexing by means of a Bayesian regression model. Inf. Process. Manag. 32(3), pp. 329-344, 1996. DOI: 10.1016/0306- 4573(95)00055-0 Castaño, E. y Melo, L.F., Métodos de combinación de pronósticos: una aplicación a la inflación colombiana. Borradores Econ. Banco de la República. 109, 1998 Bates J.M. and Granger, C.W., The combination of forecasts. Operational Research Quarterly. 20(4), pp. 451, 1969 Chan, C.K., Kingsman, B.G. and Wong, H., The value of combining forecasts in inventory management. A case study in banking. Eur. J. Oper. Res. 117, pp. 199-210, 1999 Bunn, D.W., Forecasting with more than one model. J. Forecast. 8(April), pp. 161-166, 1989. Fouskakis, D. and Draper, D., Stochastic optimization: a review, Int. Stat. Rev. 70(3), pp. 315-349, 2002. DOI: 10.1111/j.1751- 5823.2002.tb00174.x Silver, E., An overview of heuristic solution methods. Journal of the Operational Research Society. 55(9), pp. 936-956, DOI: 10.1057/palgrave.jors.2601758 Kapetanios, G., Marcellino, M. and Papailias, F., Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods. Comput. Stat. Data Anal. 100, pp. 369-382, 2014. DOI: 10.1016/j.csda.2015.02.017 Fernández, I. de Viana, O., Cordón, Alonso, S. y Herrera, F., La metaheurística de optimización basada en colonias de hormigas: modelos y nuevos enfoques. Optim. Intel., pp. 261-314, 2004 Sen, T. and Mathur, H.D., A new approach to solve economic dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm. Int. J. Electr. Power Energy Syst. 78, pp. 735-744, 2016. DOI: 10.1016/j.ijepes.2015.11.121 Kahraman, C., Kerre, E.E. and Bozbura, F.T., Uncertainty modeling in knowledge engineering and decision making. 7. World scientific, 2012. DOI: 10.1142/8564 Dorie, V., Package ‘blme’. pp. 1-8, 2015. Disponible en: https://cran.r-project.org/web/packages/blme/index.html Petris, G., Petrone, S. and Campagnoli, P., Dynamic linear models with R. 2009. DOI: 10.1007/b135794 Petris, G., An R package for dynamic linear models. J. Stat. Softw. 36(12), pp. 1-16, 2010. DOI: 10.18637/jss.v036.i12 |
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Higuita Alzate, Davidb7d1dcdd-aa54-4ca2-84ae-13de5a679bb6Valencia Cárdenas, Marisol7d8db2ff-5466-4ebb-a8b6-daf0bdc169d8Correa Morales, Juan Carlos80dd1582-58bc-447f-85ba-6aaadd85c30a2023-05-01T19:20:02Z2023-05-01T19:20:02Z20180012-7353https://dspace.tdea.edu.co/handle/tdea/28432346-2183Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector. Keywords: statistics and probability; forecasts; optimization theory; Bayesian statistics.La planeación de pronósticos de demanda de productos perecederos es importante para todo tipo de industria que los manufacture o distribuya, en especial, cuando ésta tiene un comportamiento estacional y variabilidad difícil de predecir. En este trabajo se propone una metaheurística basada en Optimización por Colonia de Hormigas (ACO) para la combinación de pronósticos de múltiples productos, basada en tres modelos: Modelo Lineal Mixto (MLM), Modelo de Regresión Bayesiana (BRM) y Modelo Bayesiano Lineal Dinámico (BDLM), los cuales hacen parte de la combinación propuesta cuyo proceso se basa en la minimización del indicador de Media de Error Absoluto Porcentual Simétrico (SMAPE). Se encuentra que las metodologías de BDLM y de BRM obtienen buenos resultados de forma individual siendo mejor esta última, no obstante, el algoritmo ACO diseñado arroja un mejor resultado, facilitando una adecuada predicción de demanda de varios productos de una empresa del sector de cárnicos. Palabras clave: estadística y probabilidad; pronósticos; teoría de optimización; estadística Bayesiana9 páginasapplication/pdfspaUniversidad Nacional de ColombiaColombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2file:///C:/Users/user/Downloads/Metodo_de_combinacion_de_pronosticos_usando_modelo.pdfForecastsProductos perecederosPerishable productsProduto perecívelProduit périssableStatistics and probabilityEstadística y probabilidadPronósticosOptimization theoryTeoría de optimizaciónBayesian statisticsEstadística BayesianaCombination forecasting method using Bayesian models and a metaheuristic, case studyMétodo de combinación de pronósticos usando modelos Bayesianos y una metaheurística, caso de estudioArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Medellín, Colombia34520733785DynaDoganis, P., Alexandridis, A., Patrinos P. and Sarimveis, H., Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J. Food Eng. 75(2), pp. 196-204, 2006. DOI:10.1016/j.jfoodeng.2005.03.056Vidal, C., Londoño, J.C. y Contreras, F., Aplicación de modelos de inventarios en una cadena de abastecimiento de productos de consumo masivo con una bodega y N puntos de venta. Ing. y Compet. 6(1), pp. 35-52, 2004.Krajewski, L., Ritzman, L. and Malhotra, M., Administración de operaciones, 8th ed. 2008Bowerman, B., Koehler, A. and O’Connell, R., Pronósticos, series de tiempo y regresión: un enfoque aplicado. México, DF. CENCAGE Learning, 2007Congdon, P., Bayesian statistical modelling. London, England: Wiley Series in Probability and Statistics, 2002.Montoya, A., Montoya, I. y Castellanos, O., Situación de la competitividad de las Pyme en Colombia : elementos actuales y retos. Agron. Colomb. 28 (1), pp. 107-117, 2010.Valencia, M., Tróchez, J., Vanegas, J. y Restrepo, J., Modelo para el análisis de la quiebra financiera en Pymes agroindustriales antioqueñas. Apunt. del CENES. 35. Julio-Diciembre, pp. 147-168, 2016.Hanke, J. y Wichern, D., Pronósticos en los negocios. Prentice Hall. 2006Valencia, M., Correa, J.C., Díaz, F. and Ramírez, S., Aplicación de modelación bayesiana y optimización para pronósticos de demanda. Ing. y Desarro. Univ. del Norte. 32(2), pp. 179-199, 2014. DOI: 10.14482/inde.32.2.5403Fei, X., Lu, C.-C. and Liu, K., A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp. Res. Part C Emerg. Technol. 19(6), pp. 1306-1318, 2011. DOI: 10.1016/j.trc.2010.10.005Sakauchi, T., Applying bayesian forecasting to predict new customers’ heating oil demand, 2011.West, B., Welch K. and Galecki, A., Linear mixed models: a practical guide using statistical software. 2006. DOI: 10.1080/15598608.2012.695708Valencia, M., Estimación en modelos lineales mixtos con datos continuos usando transformaciones y distribuciones no normales. Tesis de Maestría. 2010.Hsiao, C. and S.K., Wan Is there an optimal forecast combination?. J. Econom. 178, PART 2, pp. 294-309, 2014. DOI: 10.1016/j.jeconom.2013.11.003Chahkoutahi, F. and Khashei, M., A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy. 140, pp. 988-1004, 2017. DOI: 10.1016/j.energy.2017.09.009Gao, W., Sarlak, V., Parsaei, M. R. and Ferdosi, M. Combination of fuzzy based on a meta-heuristic algorithm to predict electricity price in an electricity markets. Chem. Eng. Res. Des. 131, pp. 333-345, 2018. DOI: 10.1016/j.cherd.2017.09.021Guerrero, L., Gómez, J.D., Zapata, D. and Valencia, M., Comparación de tres metaheurísticas para la optimización de inventarios con estimación de demanda. pp. 51-68, 2016.Makridakis, S., Hibon, M. and Moser, C., Accuracy of forecasting: an empirical investigation. J. R. Stat. Society. 142(2), pp. 97-145, 1979. DOI: 10.2307/2345077Makridakis, S., Wheelwright, S. and McGee, V., Forecasting, methods and applications, Second Ed. John Wiley & Sons, 1983Simchi-Levi, D., Kaminski, P. and Simchi-Levi, E., Designing and managing the supply chain, 3rd ed. New York: McGraw-Hill, 2008.Valencia, M., González, D. y Cardona, J., Metodología de un modelo de optimización para el pronóstico y manejo de inventarios usando el metaheurístico Tabú. Rev. Ing. 24(1), pp. 13-27, 2014. DOI: 10.15517/ring.v24i1.13771Diebold, F.X. The past, present, and future of macroeconomic forecasting. J. Econ. Perspect. 12(2), pp. 175-192, 1998.Medina, S. y García, J., Predicción de demanda de energía en Colombia mediante un sistema de inferencia difuso neuronal. Energética. 33, pp. 15-24, 2005. DOI: 10.15446/energeticaKeles, D., Scelle, J., Paraschiv, F. and Fichtner, W., Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy. 162, pp. 218-230, 2016Claveria, O. and Torra, S., Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ. Model. 36, pp. 220-228, 2014.West, B., Welch, K. and Galecki, A., Linear mixed models: a practical guide using statistical software, first. 2006Valencia, M., Estimación en modelos lineales mixtos con datos continuos usando transformaciones y distribuciones no normales. Tesis de grado. Universidad Nacional de Colombia, Sede Medellín. [en línea]. Disponible en: http://www.bdigital.unal.edu.co/1862/1/71680093.2010.pdf, 2010.Barbosa, C., Queiroz, C. and Migon, H., A dynamic linear model with extended skew-normal for the initial distribution of the state parameter. Comput. Stat. Data Anal. 74, pp. 64-80, 2014. DOI: 10.1016/j.csda.2013.12.008Oracle., The Bayesian approach to forecasting. September, 2006Kociecki, A., Kolasa, M. and Rubaszek, M., A Bayesian method of combining judgmental and model-based density forecasts. Econ. 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 incorporada 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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