Early warning method for the commodity prices based on artificial neural networks: SMEs case

Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the accuracy of traditional forecasting systems. The present study compares the perf...

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Autores:
Silva, Jesus
MOJICA HERAZO, JULIO CESAR
Rojas Millán, Rafael Humberto
Pineda Lezama, Omar Bonerge
Morgado Gamero, W.B.
Varela Izquierdo, Noel
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4840
Acceso en línea:
https://hdl.handle.net/11323/4840
https://repositorio.cuc.edu.co/
Palabra clave:
predictive model
Multilayer Perceptron
Multiple input multiple output
Forecast
Support vector machines
Cyclic variation
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/4840
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repository_id_str
dc.title.spa.fl_str_mv Early warning method for the commodity prices based on artificial neural networks: SMEs case
title Early warning method for the commodity prices based on artificial neural networks: SMEs case
spellingShingle Early warning method for the commodity prices based on artificial neural networks: SMEs case
predictive model
Multilayer Perceptron
Multiple input multiple output
Forecast
Support vector machines
Cyclic variation
title_short Early warning method for the commodity prices based on artificial neural networks: SMEs case
title_full Early warning method for the commodity prices based on artificial neural networks: SMEs case
title_fullStr Early warning method for the commodity prices based on artificial neural networks: SMEs case
title_full_unstemmed Early warning method for the commodity prices based on artificial neural networks: SMEs case
title_sort Early warning method for the commodity prices based on artificial neural networks: SMEs case
dc.creator.fl_str_mv Silva, Jesus
MOJICA HERAZO, JULIO CESAR
Rojas Millán, Rafael Humberto
Pineda Lezama, Omar Bonerge
Morgado Gamero, W.B.
Varela Izquierdo, Noel
dc.contributor.author.spa.fl_str_mv Silva, Jesus
MOJICA HERAZO, JULIO CESAR
Rojas Millán, Rafael Humberto
Pineda Lezama, Omar Bonerge
Morgado Gamero, W.B.
Varela Izquierdo, Noel
dc.subject.spa.fl_str_mv predictive model
Multilayer Perceptron
Multiple input multiple output
Forecast
Support vector machines
Cyclic variation
topic predictive model
Multilayer Perceptron
Multiple input multiple output
Forecast
Support vector machines
Cyclic variation
description Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the accuracy of traditional forecasting systems. The present study compares the performance of traditional models against other more developed systems such as ANN, and Support Vector Machines or Support Vector Regression (SVM-SVR) machines. It demonstrates the importance of considering external factors such as macroeconomic and microeconomic indicators, like the prices of related products, which affect the level of sales in an organization. The data was collected from a group of supermarkets belonging to the SMEs sector in Colombia. At first, a pre-processing was carried out to clean, adapt and standardize data bases. Then, since there was no labeled information about the pairs of substitute or complementary products, it was necessary to implement a cross-elasticity analysis. In addition, a harmonic average (f1-score) was considered at several points to establish priorities in some products and obtained results. The model proposed in this study shows its potential application in the product sales forecasting with high rotation in SMEs supermarkets since their results are more accurate than those obtained using traditional procedures.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T14:15:25Z
dc.date.available.none.fl_str_mv 2019-06-10T14:15:25Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 0000-2010
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv [1] Sanclemente, J. “Las ventas y el mercadeo, actividades indisociables y de gran impacto social y económico.: El aporte de Tosdal”, Innovar, vol. 17, núm. 30, pp. 160–162, jul. 2007. [2] Amelec, V., & Alexander, P. (2015). Improvements in the Automatic Distribution Process of Finished Product for Pet Food Category in Multinational Company. Advanced Science Letters, 21(5), 1419-1421. [3] Ayala, S. “La Economía como Ciencia, Objeto y Categorías Fundamentales”, 2015. [4] Atsalakis, G and Valavanis, K, “Surveying stock market forecasting techniques – Part II: Soft computing methods”, Expert Systems with Applications, vol. 36, núm. 3, Part 2, pp. 5932–5941, abr. 2009. [5] Matich, D. “Redes Neuronales: Conceptos básicos y aplicaciones”, Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I, 2001. [6] Viloria, A., & Robayo, P. V. (2016). Inventory reduction in the supply chain of finished products for multinational companies. Indian Journal of Science and Technology, 8(1). [7] Zhang, G. “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, vol. 50, núm. Supplement C, pp. 159–175, ene. 2003. [8] Toro, E; Mejia, D and Salazar, H. “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004. [9] Vitez, O. “Cuáles se consideran los principales indicadores económicos”, 2017. [En línea]. Disponible en: https://pyme.lavoztx.com/culesse-consideran-los-principales-indicadores-econmicos- 9641.html. [Consultado: 07-dic-2017]. [10] Wu, Q; Yan, H and Yang, H. “A Forecasting Model Based Support Vector Machine and Particle Swarm Optimization”, en 2008 Workshop on Power Electronics and Intelligent Transportation System, 2008, pp. 218–222. [11] Sapankevych, N and Sankar, R. “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. [12] Villada, F; Muñoz,N and García,E. “Aplicación de las Redes Neuronales al Pronóstico de Precios en el Mercado de Valor es”, Información tecnológica, vol. 23, núm. 4, pp. 11–20, ene. 2012. [13] Ruan, D. Fuzzy Systems and Soft Computing in Nuclear Engineering. Physica, 2013. [14] Lis-Gutiérrez JP., Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [15] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 [16] Garcia, M. “Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales”, Universitas Scientiarum, vol. 8, pp. 45–50, 2003. [17] Hanke, J and Wichern, D. Pronósticos en los negocios. Pearson Educación, 2006. [18] Obando, J. Elementos de Microeconomía. EUNED, 2000.
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spelling Silva, JesusMOJICA HERAZO, JULIO CESARRojas Millán, Rafael HumbertoPineda Lezama, Omar BonergeMorgado Gamero, W.B.Varela Izquierdo, Noel2019-06-10T14:15:25Z2019-06-10T14:15:25Z20190000-2010https://hdl.handle.net/11323/4840Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Applications based on Artificial Neural Networks (ANN) have been developed thanks to the advance of the technological progress which has permitted the development of sales forecasting on consumer products, improving the accuracy of traditional forecasting systems. The present study compares the performance of traditional models against other more developed systems such as ANN, and Support Vector Machines or Support Vector Regression (SVM-SVR) machines. It demonstrates the importance of considering external factors such as macroeconomic and microeconomic indicators, like the prices of related products, which affect the level of sales in an organization. The data was collected from a group of supermarkets belonging to the SMEs sector in Colombia. At first, a pre-processing was carried out to clean, adapt and standardize data bases. Then, since there was no labeled information about the pairs of substitute or complementary products, it was necessary to implement a cross-elasticity analysis. In addition, a harmonic average (f1-score) was considered at several points to establish priorities in some products and obtained results. The model proposed in this study shows its potential application in the product sales forecasting with high rotation in SMEs supermarkets since their results are more accurate than those obtained using traditional procedures.Silva, Jesus-60750872-819f-4163-bbb8-c33aee0e2cf1-0MOJICA HERAZO, JULIO CESAR-will be generated-orcid-0000-0003-2078-7843-600Rojas Millán, Rafael Humberto-0000-0002-4997-9040-600Pineda Lezama, Omar Bonerge-365a03a0-145e-4df5-9abe-f5ccf9d96612-0Morgado Gamero, W.B.-b5fa573c-d09e-4eeb-a2a0-e880a90f9988-0Varela Izquierdo, Noel-0000-0001-7036-4414-600engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2predictive modelMultilayer PerceptronMultiple input multiple outputForecastSupport vector machinesCyclic variationEarly warning method for the commodity prices based on artificial neural networks: SMEs caseArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Sanclemente, J. “Las ventas y el mercadeo, actividades indisociables y de gran impacto social y económico.: El aporte de Tosdal”, Innovar, vol. 17, núm. 30, pp. 160–162, jul. 2007. [2] Amelec, V., & Alexander, P. (2015). Improvements in the Automatic Distribution Process of Finished Product for Pet Food Category in Multinational Company. Advanced Science Letters, 21(5), 1419-1421. [3] Ayala, S. “La Economía como Ciencia, Objeto y Categorías Fundamentales”, 2015. [4] Atsalakis, G and Valavanis, K, “Surveying stock market forecasting techniques – Part II: Soft computing methods”, Expert Systems with Applications, vol. 36, núm. 3, Part 2, pp. 5932–5941, abr. 2009. [5] Matich, D. “Redes Neuronales: Conceptos básicos y aplicaciones”, Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I, 2001. [6] Viloria, A., & Robayo, P. V. (2016). Inventory reduction in the supply chain of finished products for multinational companies. Indian Journal of Science and Technology, 8(1). [7] Zhang, G. “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, vol. 50, núm. Supplement C, pp. 159–175, ene. 2003. [8] Toro, E; Mejia, D and Salazar, H. “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004. [9] Vitez, O. “Cuáles se consideran los principales indicadores económicos”, 2017. [En línea]. Disponible en: https://pyme.lavoztx.com/culesse-consideran-los-principales-indicadores-econmicos- 9641.html. [Consultado: 07-dic-2017]. [10] Wu, Q; Yan, H and Yang, H. “A Forecasting Model Based Support Vector Machine and Particle Swarm Optimization”, en 2008 Workshop on Power Electronics and Intelligent Transportation System, 2008, pp. 218–222. [11] Sapankevych, N and Sankar, R. “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. [12] Villada, F; Muñoz,N and García,E. “Aplicación de las Redes Neuronales al Pronóstico de Precios en el Mercado de Valor es”, Información tecnológica, vol. 23, núm. 4, pp. 11–20, ene. 2012. [13] Ruan, D. Fuzzy Systems and Soft Computing in Nuclear Engineering. Physica, 2013. [14] Lis-Gutiérrez JP., Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [15] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 [16] Garcia, M. “Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales”, Universitas Scientiarum, vol. 8, pp. 45–50, 2003. [17] Hanke, J and Wichern, D. Pronósticos en los negocios. Pearson Educación, 2006. [18] Obando, J. Elementos de Microeconomía. EUNED, 2000.PublicationORIGINALEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdfEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdfapplication/pdf671003https://repositorio.cuc.edu.co/bitstreams/cfeee20d-5ea1-47ea-885e-2232448952b4/download142015ce07270f7506d2ad1dd1c2601fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/852d3b15-ce56-467c-92d6-326c0029cf63/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/11ea3e0b-3db5-49b4-83a0-56bfe6125652/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdf.jpgEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdf.jpgimage/jpeg50156https://repositorio.cuc.edu.co/bitstreams/1d95f573-9615-4980-99af-737997c9df67/downloade0c54feebbeb7963a1cbe80eddedcff0MD55TEXTEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdf.txtEarly warning method for the commodity prices based on artificial neural networks SMEs case.pdf.txttext/plain22924https://repositorio.cuc.edu.co/bitstreams/ebc2075e-25e3-433a-817d-b1f8069220e7/download5c98a49555dc7f2b049ed1d287b358fdMD5611323/4840oai:repositorio.cuc.edu.co:11323/48402024-09-17 10:50:01.29http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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