An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks

The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and c...

Full description

Autores:
Silva, Jesús
Varela, Noel
Martínez Caraballo, Hugo
García Guiliany, Jesús
Cabas Vásquez, Luis Carlos
Navarro Beltrán, Jorge
León Castro, Nadia
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5133
Acceso en línea:
https://hdl.handle.net/11323/5133
https://repositorio.cuc.edu.co/
Palabra clave:
Forecast
Multiple Input Multiple Output
Multilayer perceptron
Predictive model
Cyclic variation
Support vector machines
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_814555a7244b4e28f010e1d8fa8f9ff2
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5133
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
title An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
spellingShingle An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
Forecast
Multiple Input Multiple Output
Multilayer perceptron
Predictive model
Cyclic variation
Support vector machines
title_short An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
title_full An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
title_fullStr An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
title_full_unstemmed An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
title_sort An Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks
dc.creator.fl_str_mv Silva, Jesús
Varela, Noel
Martínez Caraballo, Hugo
García Guiliany, Jesús
Cabas Vásquez, Luis Carlos
Navarro Beltrán, Jorge
León Castro, Nadia
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Varela, Noel
Martínez Caraballo, Hugo
García Guiliany, Jesús
Cabas Vásquez, Luis Carlos
Navarro Beltrán, Jorge
León Castro, Nadia
dc.subject.spa.fl_str_mv Forecast
Multiple Input Multiple Output
Multilayer perceptron
Predictive model
Cyclic variation
Support vector machines
topic Forecast
Multiple Input Multiple Output
Multilayer perceptron
Predictive model
Cyclic variation
Support vector machines
description The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-08-08T14:42:39Z
dc.date.available.none.fl_str_mv 2019-08-08T14:42:39Z
dc.date.issued.none.fl_str_mv 2019-06-26
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.isbn.spa.fl_str_mv 978-3-030-22795-1
978-3-030-22796-8
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/5133
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/
identifier_str_mv 978-3-030-22795-1
978-3-030-22796-8
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5133
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1007/978-3-030-22796-8_38
dc.relation.references.spa.fl_str_mv 1. Fonseca, Z., et al.: Encuesta Nacional de la Situación Nutricional en Colombia 2010. Da Vinci, Bogotá (2011) Google Scholar 2. Instituto Colombiano de Bienestar Familiar (ICBF): Ministerio de Salud y Protección Social, Instituto Nacional de Salud (INS), Departamento Administrativo para la Prosperidad Social, Universidad Nacional de Colombia. The National Survey of the Nutritional Situation of Colombia (ENSIN) (2015) Google Scholar 3. Food and Agriculture Organization of the United Nations (FAO): Pan American Health Organization (PAHO), World Food Programme (WFP), United nations International Children’s Emergency Fund (UNICEF). Panorama of Food and Nutritional Security in Latin America and the Caribbean, Inequality and Food Systems, Santiago (2018) Google Scholar 4. Frank, R.J., Davey, N., Hunt, S.P.: Time series prediction and neural networks. J. Intell. Rob. Syst. 31(3), 91–103 (2001) zbMATHGoogle Scholar 5. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, Upper Saddle River (2009) Google Scholar 6. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996) Google Scholar 7. Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2008) Google Scholar 8. McNelis, P.D.: Neural networks in finance: gaining predictive edge in the market, vol. 59, no. 1, pp. 1–22. Elsevier Academic Press, Massachusetts (2005) Google Scholar 9. Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015) Google Scholar 10. Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014) Google Scholar 11. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003) zbMATHGoogle Scholar 12. Horton, N.J., Kleinman, K.: Using R For Data Management, Statistical Analysis, and Graphics. CRC Press, Clermont (2010) zbMATHGoogle Scholar 13. Chang, P.C., Wang, Y.W.: Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30(4), 715–726 (2006) Google Scholar 14. Lander, J.P.: R for Everyone: Advanced Analytics and Graphics. Addison-Wesley Professional, Boston (2014) Google Scholar 15. Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning and Operation. Prentice Hall, Upper Saddle River (2001) Google Scholar 16. Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93818-9_16 Google Scholar 17. Babu, C.N., Reddy, B.E.: A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl. Soft Comput. 23(1), 27–38 (2014) Google Scholar 18. Cai, Q., Zhang, D., Wu, B., Leung, S.C.: A novel stock forecasting model based on fuzzy time series and genetic algorithm. Procedia Comput. Sci 18(1), 1155–1162 (2013) Google Scholar 19. Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40(1), 854–857 (2013) Google Scholar 20. Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural network ensemble operators for time series forecasting. Expert Syst. Appl. 41(1), 4235–4244 (2014) Google Scholar 21. Departamento Administrativo Nacional de Estadística-DANE: Manual Técnico del Censo General. DANE, Bogotá (2018) Google Scholar 22. Fajardo-Toro, C.H., Mula, J., Poler, R.: Adaptive and hybrid forecasting models—a review. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, J.-P. (eds.) Engineering Digital Transformation. LNMIE, pp. 315–322. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96005-0_38 Google Scholar 23. Deliana, Y., Rum, I.A.: Understanding consumer loyalty using neural network. Pol. J. Manag. Stud. 16(2), 51–61 (2017) Google Scholar 24. Chang, O., Constante, P., Gordon, A., Singana, M.: A novel deep neural network that uses space-time features for tracking and recognizing a moving object. J. Artif. Intell. Soft Comput. Res. 7(2), 125–136 (2017) Google Scholar 25. Scherer, M.: Waste flows management by their prediction in a production company. J. Appl. Math. Comput. Mech. 16(2), 135–144 (2017) Google Scholar 26. Sekmen, F., Kurkcu, M.: An early warning system for Turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Financ. Rev. 4(1), 529–543 (2014) Google Scholar 27. Ke, Y., Hagiwara, M.: An English neural network that learns texts, finds hidden knowledge, and answers questions. J. Artif. Intell. Soft Comput. Res. 7(4), 229–242 (2017) Google Scholar
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spelling Silva, JesúsVarela, NoelMartínez Caraballo, HugoGarcía Guiliany, JesúsCabas Vásquez, Luis CarlosNavarro Beltrán, JorgeLeón Castro, Nadia2019-08-08T14:42:39Z2019-08-08T14:42:39Z2019-06-26978-3-030-22795-1978-3-030-22796-8https://hdl.handle.net/11323/5133Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors.Silva, JesúsVarela, NoelMartínez Caraballo, HugoGarcía Guiliany, JesúsCabas Vásquez, Luis CarlosNavarro Beltrán, JorgeLeón Castro, NadiaengInternational Symposium on Neural Networkshttps://doi.org/10.1007/978-3-030-22796-8_381. Fonseca, Z., et al.: Encuesta Nacional de la Situación Nutricional en Colombia 2010. Da Vinci, Bogotá (2011) Google Scholar 2. Instituto Colombiano de Bienestar Familiar (ICBF): Ministerio de Salud y Protección Social, Instituto Nacional de Salud (INS), Departamento Administrativo para la Prosperidad Social, Universidad Nacional de Colombia. The National Survey of the Nutritional Situation of Colombia (ENSIN) (2015) Google Scholar 3. Food and Agriculture Organization of the United Nations (FAO): Pan American Health Organization (PAHO), World Food Programme (WFP), United nations International Children’s Emergency Fund (UNICEF). Panorama of Food and Nutritional Security in Latin America and the Caribbean, Inequality and Food Systems, Santiago (2018) Google Scholar 4. Frank, R.J., Davey, N., Hunt, S.P.: Time series prediction and neural networks. J. Intell. Rob. Syst. 31(3), 91–103 (2001) zbMATHGoogle Scholar 5. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall International, Upper Saddle River (2009) Google Scholar 6. Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Comput. 29(3), 1–32 (1996) Google Scholar 7. Kulkarni, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–89 (2008) Google Scholar 8. McNelis, P.D.: Neural networks in finance: gaining predictive edge in the market, vol. 59, no. 1, pp. 1–22. Elsevier Academic Press, Massachusetts (2005) Google Scholar 9. Mombeini, H., Yazdani-Chamzini, A.: Modelling gold price via artificial neural network. J. Econ. Bus. Manag. 3(7), 699–703 (2015) Google Scholar 10. Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(1), 1095–1104 (2014) Google Scholar 11. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(1), 159–175 (2003) zbMATHGoogle Scholar 12. Horton, N.J., Kleinman, K.: Using R For Data Management, Statistical Analysis, and Graphics. CRC Press, Clermont (2010) zbMATHGoogle Scholar 13. Chang, P.C., Wang, Y.W.: Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30(4), 715–726 (2006) Google Scholar 14. Lander, J.P.: R for Everyone: Advanced Analytics and Graphics. Addison-Wesley Professional, Boston (2014) Google Scholar 15. Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning and Operation. Prentice Hall, Upper Saddle River (2001) Google Scholar 16. Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93818-9_16 Google Scholar 17. Babu, C.N., Reddy, B.E.: A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl. Soft Comput. 23(1), 27–38 (2014) Google Scholar 18. Cai, Q., Zhang, D., Wu, B., Leung, S.C.: A novel stock forecasting model based on fuzzy time series and genetic algorithm. Procedia Comput. Sci 18(1), 1155–1162 (2013) Google Scholar 19. Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40(1), 854–857 (2013) Google Scholar 20. Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural network ensemble operators for time series forecasting. Expert Syst. Appl. 41(1), 4235–4244 (2014) Google Scholar 21. Departamento Administrativo Nacional de Estadística-DANE: Manual Técnico del Censo General. DANE, Bogotá (2018) Google Scholar 22. Fajardo-Toro, C.H., Mula, J., Poler, R.: Adaptive and hybrid forecasting models—a review. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, J.-P. (eds.) Engineering Digital Transformation. LNMIE, pp. 315–322. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96005-0_38 Google Scholar 23. Deliana, Y., Rum, I.A.: Understanding consumer loyalty using neural network. Pol. J. Manag. Stud. 16(2), 51–61 (2017) Google Scholar 24. Chang, O., Constante, P., Gordon, A., Singana, M.: A novel deep neural network that uses space-time features for tracking and recognizing a moving object. J. Artif. Intell. Soft Comput. Res. 7(2), 125–136 (2017) Google Scholar 25. Scherer, M.: Waste flows management by their prediction in a production company. J. Appl. Math. Comput. Mech. 16(2), 135–144 (2017) Google Scholar 26. Sekmen, F., Kurkcu, M.: An early warning system for Turkey: the forecasting of economic crisis by using the artificial neural networks. Asian Econ. Financ. Rev. 4(1), 529–543 (2014) Google Scholar 27. Ke, Y., Hagiwara, M.: An English neural network that learns texts, finds hidden knowledge, and answers questions. J. Artif. Intell. Soft Comput. Res. 7(4), 229–242 (2017) Google ScholarCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2ForecastMultiple Input Multiple OutputMultilayer perceptronPredictive modelCyclic variationSupport vector machinesAn Early Warning Method for Basic Commodities Price Based on Artificial Neural NetworksPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdfAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdfapplication/pdf488904https://repositorio.cuc.edu.co/bitstreams/88de91ed-bc6d-43f4-9c78-1c6a323bc40d/download6707846cbb94b0cbb043a645de0f8cd4MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/ba931d9b-822e-4dfa-b7b4-ae4a47004ca6/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/d47b5ed7-6182-4442-941c-b9c7fb0a0311/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdf.jpgAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdf.jpgimage/jpeg46450https://repositorio.cuc.edu.co/bitstreams/b795bde5-b522-4066-890d-b02497f177d1/download29e1dbc4155ad7e42eff28e267886dc6MD55TEXTAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdf.txtAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networks.pdf.txttext/plain27614https://repositorio.cuc.edu.co/bitstreams/1409e02f-cb5c-4e28-85b4-da1e92a5c5d0/download478f60af0afa2c264d25706afe588f0aMD5611323/5133oai:repositorio.cuc.edu.co:11323/51332024-09-17 11:08:44.035http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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