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...
- 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:
- http://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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| 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 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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0000-2010 |
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http://hdl.handle.net/11323/4840 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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0000-2010 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
| language |
eng |
| 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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Silva, Jesus659ae35f3326439474c6cca46ee77cb0MOJICA HERAZO, JULIO CESAR0cebcba562ef5fb145686b46b18cbd5eRojas Millán, Rafael Humberto7fb73bb8a2255f7b798b261b5e5548b2Pineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bdMorgado Gamero, W.B.8c7e2b561c31e152bfc4c80e74124591Varela Izquierdo, Noel484160b66adc1de7303e235ec78945322019-06-10T14:15:25Z2019-06-10T14:15:25Z20190000-2010http://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.engProcedia 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. 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