Colombian Coffee Price Forecast via LSTM Neural Networks
This work deals with the contributions Machine Learning techniques can bring into the coffee growing conglomerate, committees and other points in the production and marketing chain involved in the dynamics of this commodity. It is well known that the different variables that interact with prices bot...
- Autores:
-
Herrera Jaramillo, Yoe Alexander
Ortega Giraldo, Johana C.
Acevedo Amorocho, Alejandro
Prada Marín, Duwamg Alexis
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2021
- Institución:
- Tecnológico de Antioquia
- Repositorio:
- Repositorio Tdea
- Idioma:
- eng
- OAI Identifier:
- oai:dspace.tdea.edu.co:tdea/3958
- Acceso en línea:
- https://dspace.tdea.edu.co/handle/tdea/3958
- Palabra clave:
- Aprendizado de máquina
Café
Coffee
Precios
Prix
Prices
Preço
Machine learning
Aprendizaje automático
Apprentissage machine
Neural Networks, Computer
Redes Neurais de Computação
Redes Neurales de la Computación
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Colombian Coffee Price Forecast via LSTM Neural Networks |
| title |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| spellingShingle |
Colombian Coffee Price Forecast via LSTM Neural Networks Aprendizado de máquina Café Coffee Precios Prix Prices Preço Machine learning Aprendizaje automático Apprentissage machine Neural Networks, Computer Redes Neurais de Computação Redes Neurales de la Computación |
| title_short |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| title_full |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| title_fullStr |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| title_full_unstemmed |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| title_sort |
Colombian Coffee Price Forecast via LSTM Neural Networks |
| dc.creator.fl_str_mv |
Herrera Jaramillo, Yoe Alexander Ortega Giraldo, Johana C. Acevedo Amorocho, Alejandro Prada Marín, Duwamg Alexis |
| dc.contributor.author.none.fl_str_mv |
Herrera Jaramillo, Yoe Alexander Ortega Giraldo, Johana C. Acevedo Amorocho, Alejandro Prada Marín, Duwamg Alexis |
| dc.subject.classification.none.fl_str_mv |
Aprendizado de máquina |
| topic |
Aprendizado de máquina Café Coffee Precios Prix Prices Preço Machine learning Aprendizaje automático Apprentissage machine Neural Networks, Computer Redes Neurais de Computação Redes Neurales de la Computación |
| dc.subject.agrovoc.none.fl_str_mv |
Café Coffee Precios Prix Prices Preço |
| dc.subject.decs.none.fl_str_mv |
Machine learning Aprendizaje automático Apprentissage machine Neural Networks, Computer Redes Neurais de Computação Redes Neurales de la Computación |
| description |
This work deals with the contributions Machine Learning techniques can bring into the coffee growing conglomerate, committees and other points in the production and marketing chain involved in the dynamics of this commodity. It is well known that the different variables that interact with prices both nationally and internationally have a direct, dramatic affect on the sector under study. In this work, we summarize an extensive review of the coffee price dynamics and the forecast techniques used in this eld. In addition, the internal coffee price in Colombia has been modeled using a long short-term memory (LSTM) recurrent neural network that was chosen as the one of better performance out of three original models. The archetype that evidenced a pertinent superiority of fitness within the parameters specified for this type of model is composed of a linear self-regressive component, plus a multi-layer perceptron-type artificial neural network with twenty (40) LSTM cells neurons in the hidden layer. This epitome captures the chaotic coffee price dynamics. The normalized residuals of the model are uncorrelated and homoscedastic and follow a normal distribution. The results indicate that the current price depends on the prices that occurred in the last four (4) years. This tool can be used to help the coffee growing community to better design alternatives to overcome difficulties with the price of the grain, and this makes it a Logistics solution for them. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
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2023-10-12T23:15:01Z |
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2023-10-12T23:15:01Z |
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Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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Text |
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info:eu-repo/semantics/bookPart |
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http://purl.org/redcol/resource_type/CAP_LIB |
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978-3-030-68654-3 |
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eng |
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eng |
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Lecture Notes in Intelligent Transportation and Infrastructure; |
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517 |
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501 |
| dc.relation.ispartofbook.spa.fl_str_mv |
Technological and Industrial Applications Associated with Intelligent Logistics |
| dc.relation.references.spa.fl_str_mv |
Wei L-Y (2013) A hybrid model based on anfis and adaptive expectation genetic algorithm to forecast taiex. Econ Modelling, 33:893–899. ISSN 0264-9993. https://doi.org/10.1016/j.econmod.2013.06.009. http://www.sciencedirect.com/science/article/pii/S0264999313002253 Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2): 383–417. ISSN 00221082, 15406261. http://www.jstor.org/stable/2325486 Sierra Suarez KJ, Duarte Duarte JB, Rueda Ortz VA (2015) Predictibilidad de los retornos en el mercado de colombia e hipotesis de mercado adaptativo. Estudios Gerenciales 31(137):411–418. ISSN 0123-5923. https://doi.org/10.1016/j.estger.2015.05.004. http://www.sciencedirect.com/science/article/pii/S0123592315000340 Ramiah V, Xu X, Moosa IA (2015) Neoclassical finance, behavioral finance and noise traders: a review and assessment of the literature. Int Rev Financ Anal 41:89–100. ISSN 1057-5219. doi: https://doi.org/10.1016/j.irfa.2015.05.021. http://www.sciencedirect.com/science/article/pii/S1057521915001039 Khashei M, Hajirahimi Z (2017) Performance evaluation of series and parallel strategies for financial time series forecasting. Financ Innov 3(1): 24, Nov. ISSN 2199-4730. https://doi.org/10.1186/s40854-017-0074-9 Albertus M (2019) The effect of commodity price shocks on public lands distribution: evidence from colombia. World Dev 113: 294–308. ISSN 0305-750X. https://doi.org/10.1016/j.worlddev.2018.09.012. http://www.sciencedirect.com/science/article/pii/S0305750X1830336X Sephton PS (2019) El nino, la nina, and a cup of joe. Energy Econ 84:104503. ISSN 0140-9883. https://doi.org/10.1016/j.eneco.2019.104503. http://www.sciencedirect.com/science/article/pii/S0140988319302841 International Trade Centre (2012) Climate change and the coffee industry. ITC, Geneva. http://www.intracen.org/publicacion/Climate-change-and-the-coffee-industry1-en/ Odell JS (1997) Open-economy politics: the political economy of the world coffee trade. In: Bates RH. Princeton university press, Princeton, NJ, 221p. 59:00cloth; 18.95 paper. American Political Science Review, 95(1):250–251, 2001. https://doi.org/10.1017/S000305540175201X Mehta A, Chavas J-P (2008) Responding to the coffee crisis: What can we learn from price dynamics? J Dev Econ 85(1):282–311. ISSN 0304-3878. https://doi.org/10.1016/j.jdeveco.2006.07.006. http://www.sciencedirect.com/science/article/pii/S0304387806001349 Rodriguez A, Melgarejo M (2020) Identification of colombian coffee price dynamics. Chaos: An Interdiscipl J Non-linear Sci 30(1):013145. https://doi.org/10.1063/1.5119857 Juan Velasquez Henao MAD (2007) Modelado del precio del cafe colombiano en la bolsa de nueva york usando redes neuronales artificiales. Revista Facultad Nacional de Agronom a Medellin 60(2):4129–4144. https://revistas.unal.edu.co/index.php/refame/article/view/24463. ISSN 2248-7026 Berhane T, Shibabaw N, Shibabaw A, Adam M, Muhamed AA (2018) Forecasting the Ethiopian coffee price using kalman filtering algorithm. J Resour Ecol 9(3): 302–305. https://doi.org/10.5814/j.issn.1674-764x.2018.03.010 Shumway RH, Stoffer DS (2005) Time series analysis and its applications (Springer Texts in Statistics). Springer-Verlag, Berlin, Heidelberg ISBN 0387989501 Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088): 533–536. ISSN 1476-4687. https://doi.org/10.1038/323533a0 Wytho BJ (1993) Backpropagation neural networks: a tutorial. Chemometr Intell Lab Syst 18(2):115–155. ISSN 0169-7439. https://doi.org/10.1016/0169-7439(93)80052-J. http://www.sciencedirect.com/science/article/pii/016974399380052J Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In Bengio Y, LeCun Y (eds) 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9. Conference Track Proceedings. http://arxiv.org/abs/1412.6980 Hammer B (1998) On the approximation capability of recurrent neural networks. In: International symposium on neural computation, pp 12–4 Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. ISSN 0899-7667. https://doi.org/10.1162/neco.1997.9.8.1735 Wang Y (2017) A new concept using lstm neural networks for dynamic system identification. In: 2017 American control conference (ACC), pp 5324–5329 Graves A (2012) Supervised sequence labelling with recurrent neural networks. In: Studies in Computational Intelligence. Springer, Berlin. https://doi.org/10.1007/978-3-642-24797-2. https://cds.cern.ch/record/1503877 |
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Herrera Jaramillo, Yoe Alexanderae82ab16-3913-4702-babe-4cc669e57d46Ortega Giraldo, Johana C.e9791eaf-f691-416d-bd0a-4fbf8f837b49Acevedo Amorocho, Alejandro69621cd5-240e-4dbb-8f96-4d0b54d37e9dPrada Marín, Duwamg Alexise08894b8-36bd-49c0-84ea-a01410cc8e842023-10-12T23:15:01Z2023-10-12T23:15:01Z2021978-3-030-68654-3https://dspace.tdea.edu.co/handle/tdea/3958978-3-030-68657-4This work deals with the contributions Machine Learning techniques can bring into the coffee growing conglomerate, committees and other points in the production and marketing chain involved in the dynamics of this commodity. It is well known that the different variables that interact with prices both nationally and internationally have a direct, dramatic affect on the sector under study. In this work, we summarize an extensive review of the coffee price dynamics and the forecast techniques used in this eld. In addition, the internal coffee price in Colombia has been modeled using a long short-term memory (LSTM) recurrent neural network that was chosen as the one of better performance out of three original models. The archetype that evidenced a pertinent superiority of fitness within the parameters specified for this type of model is composed of a linear self-regressive component, plus a multi-layer perceptron-type artificial neural network with twenty (40) LSTM cells neurons in the hidden layer. This epitome captures the chaotic coffee price dynamics. The normalized residuals of the model are uncorrelated and homoscedastic and follow a normal distribution. The results indicate that the current price depends on the prices that occurred in the last four (4) years. This tool can be used to help the coffee growing community to better design alternatives to overcome difficulties with the price of the grain, and this makes it a Logistics solution for them.17 páginasimage/jpegengSpringerSuizaLecture Notes in Intelligent Transportation and Infrastructure;517501Technological and Industrial Applications Associated with Intelligent LogisticsWei L-Y (2013) A hybrid model based on anfis and adaptive expectation genetic algorithm to forecast taiex. Econ Modelling, 33:893–899. ISSN 0264-9993. https://doi.org/10.1016/j.econmod.2013.06.009. http://www.sciencedirect.com/science/article/pii/S0264999313002253Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2): 383–417. ISSN 00221082, 15406261. http://www.jstor.org/stable/2325486Sierra Suarez KJ, Duarte Duarte JB, Rueda Ortz VA (2015) Predictibilidad de los retornos en el mercado de colombia e hipotesis de mercado adaptativo. Estudios Gerenciales 31(137):411–418. ISSN 0123-5923. https://doi.org/10.1016/j.estger.2015.05.004. http://www.sciencedirect.com/science/article/pii/S0123592315000340Ramiah V, Xu X, Moosa IA (2015) Neoclassical finance, behavioral finance and noise traders: a review and assessment of the literature. Int Rev Financ Anal 41:89–100. ISSN 1057-5219. doi: https://doi.org/10.1016/j.irfa.2015.05.021. http://www.sciencedirect.com/science/article/pii/S1057521915001039Khashei M, Hajirahimi Z (2017) Performance evaluation of series and parallel strategies for financial time series forecasting. Financ Innov 3(1): 24, Nov. ISSN 2199-4730. https://doi.org/10.1186/s40854-017-0074-9Albertus M (2019) The effect of commodity price shocks on public lands distribution: evidence from colombia. World Dev 113: 294–308. ISSN 0305-750X. https://doi.org/10.1016/j.worlddev.2018.09.012. http://www.sciencedirect.com/science/article/pii/S0305750X1830336XSephton PS (2019) El nino, la nina, and a cup of joe. Energy Econ 84:104503. ISSN 0140-9883. https://doi.org/10.1016/j.eneco.2019.104503. http://www.sciencedirect.com/science/article/pii/S0140988319302841International Trade Centre (2012) Climate change and the coffee industry. ITC, Geneva. http://www.intracen.org/publicacion/Climate-change-and-the-coffee-industry1-en/Odell JS (1997) Open-economy politics: the political economy of the world coffee trade. In: Bates RH. Princeton university press, Princeton, NJ, 221p. 59:00cloth; 18.95 paper. American Political Science Review, 95(1):250–251, 2001. https://doi.org/10.1017/S000305540175201XMehta A, Chavas J-P (2008) Responding to the coffee crisis: What can we learn from price dynamics? J Dev Econ 85(1):282–311. ISSN 0304-3878. https://doi.org/10.1016/j.jdeveco.2006.07.006. http://www.sciencedirect.com/science/article/pii/S0304387806001349Rodriguez A, Melgarejo M (2020) Identification of colombian coffee price dynamics. Chaos: An Interdiscipl J Non-linear Sci 30(1):013145. https://doi.org/10.1063/1.5119857Juan Velasquez Henao MAD (2007) Modelado del precio del cafe colombiano en la bolsa de nueva york usando redes neuronales artificiales. Revista Facultad Nacional de Agronom a Medellin 60(2):4129–4144. https://revistas.unal.edu.co/index.php/refame/article/view/24463. ISSN 2248-7026Berhane T, Shibabaw N, Shibabaw A, Adam M, Muhamed AA (2018) Forecasting the Ethiopian coffee price using kalman filtering algorithm. J Resour Ecol 9(3): 302–305. https://doi.org/10.5814/j.issn.1674-764x.2018.03.010Shumway RH, Stoffer DS (2005) Time series analysis and its applications (Springer Texts in Statistics). Springer-Verlag, Berlin, Heidelberg ISBN 0387989501Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088): 533–536. ISSN 1476-4687. https://doi.org/10.1038/323533a0Wytho BJ (1993) Backpropagation neural networks: a tutorial. Chemometr Intell Lab Syst 18(2):115–155. ISSN 0169-7439. https://doi.org/10.1016/0169-7439(93)80052-J. http://www.sciencedirect.com/science/article/pii/016974399380052JKingma DP, Ba J (2015) Adam: a method for stochastic optimization. In Bengio Y, LeCun Y (eds) 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9. Conference Track Proceedings. http://arxiv.org/abs/1412.6980Hammer B (1998) On the approximation capability of recurrent neural networks. In: International symposium on neural computation, pp 12–4Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. ISSN 0899-7667. https://doi.org/10.1162/neco.1997.9.8.1735Wang Y (2017) A new concept using lstm neural networks for dynamic system identification. In: 2017 American control conference (ACC), pp 5324–5329Graves A (2012) Supervised sequence labelling with recurrent neural networks. In: Studies in Computational Intelligence. Springer, Berlin. https://doi.org/10.1007/978-3-642-24797-2. https://cds.cern.ch/record/1503877https://link.springer.com/chapter/10.1007/978-3-030-68655-0_25Aprendizado de máquinaCaféCoffeePreciosPrixPricesPreçoMachine learningAprendizaje automáticoApprentissage machineNeural Networks, ComputerRedes Neurais de ComputaçãoRedes Neurales de la ComputaciónColombian Coffee Price Forecast via LSTM Neural NetworksCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Colombiainfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbTHUMBNAILColombian Coffee Price Forecast via LSTM Neural Networks.jpg.jpgColombian Coffee Price Forecast via LSTM Neural Networks.jpg.jpgGenerated Thumbnailimage/jpeg13897https://dspace.tdea.edu.co/bitstream/tdea/3958/3/Colombian%20Coffee%20Price%20Forecast%20via%20LSTM%20Neural%20Networks.jpg.jpg28a37d5928c6ac6c05c669797bda2870MD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://dspace.tdea.edu.co/bitstream/tdea/3958/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessORIGINALColombian Coffee Price Forecast via LSTM Neural Networks.jpgColombian Coffee Price Forecast via LSTM Neural Networks.jpgDatos del documentoimage/jpeg209519https://dspace.tdea.edu.co/bitstream/tdea/3958/1/Colombian%20Coffee%20Price%20Forecast%20via%20LSTM%20Neural%20Networks.jpg695cee1b8b31e0b2e900529772703ac5MD51open accesstdea/3958oai:dspace.tdea.edu.co:tdea/39582023-10-13 03:03:52.113open accessRepositorio Institucional Tecnologico de 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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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