Time series decomposition using automatic learning techniques for predictive models

This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series....

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Autores:
Silva, Jesús
H, H
Niebles Núñez, William
Ovallos-Gazabon, David
Varela, Noel
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6237
Acceso en línea:
https://hdl.handle.net/11323/6237
https://repositorio.cuc.edu.co/
Palabra clave:
Automatic learning
Predictive models
Production prediction
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_b174753dd02d67010f46882f0d2d8ebb
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6237
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Time series decomposition using automatic learning techniques for predictive models
title Time series decomposition using automatic learning techniques for predictive models
spellingShingle Time series decomposition using automatic learning techniques for predictive models
Automatic learning
Predictive models
Production prediction
title_short Time series decomposition using automatic learning techniques for predictive models
title_full Time series decomposition using automatic learning techniques for predictive models
title_fullStr Time series decomposition using automatic learning techniques for predictive models
title_full_unstemmed Time series decomposition using automatic learning techniques for predictive models
title_sort Time series decomposition using automatic learning techniques for predictive models
dc.creator.fl_str_mv Silva, Jesús
H, H
Niebles Núñez, William
Ovallos-Gazabon, David
Varela, Noel
dc.contributor.author.spa.fl_str_mv Silva, Jesús
H, H
Niebles Núñez, William
Ovallos-Gazabon, David
Varela, Noel
dc.subject.spa.fl_str_mv Automatic learning
Predictive models
Production prediction
topic Automatic learning
Predictive models
Production prediction
description This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition. The agricultural sector will be used as the study subject.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-23T16:32:32Z
dc.date.available.none.fl_str_mv 2020-04-23T16:32:32Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1742-6588
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6237
dc.identifier.doi.spa.fl_str_mv doi:10.1088/1742-6596/1432/1/012096
1742-6596
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv 1742-6588
doi:10.1088/1742-6596/1432/1/012096
1742-6596
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6237
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Departamento Administrativo Nacional de Estadística -DANE-. (2019). Importaciones colombianas. https://www.dane.gov.co/index.php/ comercio-exterior/importaciones
[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.
[3] Comisión Económica para América Latina y el Caribe -CEPAL-. (2013). Visión agrícola del TLC entre Colombia y Estados Unidos: preparación, negociación, implementación y aprovechamiento.
[4] Henao-Rodríguez, C., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Malagón, L. E., & Viloria, A. (2018, May). Econometric analysis of the industrial growth determinants in Colombia. In Australasian Database Conference (pp. 316-321). Springer, Cham.
[5] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., PortilloMedina R. (2018) Measures of Concentration and Stability: Two Pedagogical Tools for Industrial Organization Courses. In: Tan Y., Shi Y., Tang Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham
[6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).
[7] 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.
[8] J. C. Sanclemente, “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.
[9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.
[10] N. Swanson y H. White, “Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models”, International Journal of Forecasting, vol. 13, núm. 4, pp. 439–461, 1997.
[11] E. M. Toro, D. A. Mejia, y H. Salazar, “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004.
[12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.
[13] Akram, M., Bhatti, I., Ashfaq, M., Khan, A.A. Hierarchical Forecasts of Agronomy-Based Data, American Journal of Mathematical and Ma- nagement Sciences, 36(1), 49-65, 2017.
[14] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technolo- gies in Agriculture, Forestry and Environment, 43-47, 2011
[15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014. [16] Fukuda S., Spreer W., Yasunaga E., Yuge K., Sardsud V. and Muller J. Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes, Agricultural Water Management, 116(1), 142-150,2013.
[16] Ruß G. Data Mining of Agricultural Yield Data: A Comparison of Regression Models, In: Perner P. (eds) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2009. Lecture Notes in Computer Science, vol 5633.
[17] Taylor S. and Letham B. prophet: Automatic Forecasting Procedure. R package version 0.1. 2017
[18] Wuo W., Xue H. An incorporative statistic and neural approach for crop yield modelling and forecasting, Neural Computing and Applications, 21(1): 109–117, 2012.
[19] Ji, B., Sun Y., Yang S. and Wan J. Artificial neural networks for rice yield prediction in mountainous regions, Journal of Agricultural Science, 145: 249-26, 2007.
[20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004
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spelling Silva, JesúsH, HNiebles Núñez, WilliamOvallos-Gazabon, DavidVarela, Noel2020-04-23T16:32:32Z2020-04-23T16:32:32Z20201742-6588https://hdl.handle.net/11323/6237doi:10.1088/1742-6596/1432/1/0120961742-6596Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition. The agricultural sector will be used as the study subject.Silva, JesúsHernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Niebles Núñez, WilliamOvallos-Gazabon, DavidVarela, NoelengJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Automatic learningPredictive modelsProduction predictionTime series decomposition using automatic learning techniques for predictive modelsArtículo 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] Departamento Administrativo Nacional de Estadística -DANE-. (2019). Importaciones colombianas. https://www.dane.gov.co/index.php/ comercio-exterior/importaciones[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[3] Comisión Económica para América Latina y el Caribe -CEPAL-. (2013). Visión agrícola del TLC entre Colombia y Estados Unidos: preparación, negociación, implementación y aprovechamiento.[4] Henao-Rodríguez, C., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Malagón, L. E., & Viloria, A. (2018, May). Econometric analysis of the industrial growth determinants in Colombia. In Australasian Database Conference (pp. 316-321). Springer, Cham.[5] Lis-Gutiérrez JP., Gaitán-Angulo M., Henao L.C., Viloria A., Aguilera-Hernández D., PortilloMedina R. (2018) Measures of Concentration and Stability: Two Pedagogical Tools for Industrial Organization Courses. In: Tan Y., Shi Y., Tang Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham[6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).[7] 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.[8] J. C. Sanclemente, “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.[9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.[10] N. Swanson y H. White, “Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models”, International Journal of Forecasting, vol. 13, núm. 4, pp. 439–461, 1997.[11] E. M. Toro, D. A. Mejia, y H. Salazar, “Pronóstico de ventas usando redes neuronales”, Scientia et technica, vol. 10, núm. 26, 2004.[12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.[13] Akram, M., Bhatti, I., Ashfaq, M., Khan, A.A. Hierarchical Forecasts of Agronomy-Based Data, American Journal of Mathematical and Ma- nagement Sciences, 36(1), 49-65, 2017.[14] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technolo- gies in Agriculture, Forestry and Environment, 43-47, 2011[15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014. [16] Fukuda S., Spreer W., Yasunaga E., Yuge K., Sardsud V. and Muller J. Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes, Agricultural Water Management, 116(1), 142-150,2013.[16] Ruß G. Data Mining of Agricultural Yield Data: A Comparison of Regression Models, In: Perner P. (eds) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2009. Lecture Notes in Computer Science, vol 5633.[17] Taylor S. and Letham B. prophet: Automatic Forecasting Procedure. R package version 0.1. 2017[18] Wuo W., Xue H. An incorporative statistic and neural approach for crop yield modelling and forecasting, Neural Computing and Applications, 21(1): 109–117, 2012.[19] Ji, B., Sun Y., Yang S. and Wan J. Artificial neural networks for rice yield prediction in mountainous regions, Journal of Agricultural Science, 145: 249-26, 2007.[20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004PublicationORIGINALTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdfTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdfapplication/pdf696092https://repositorio.cuc.edu.co/bitstreams/3794e594-dc07-4a54-844e-d7ba253f5bcc/downloada0f4a4c668b5bab9dcd75804c1770836MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/0ed20161-85e7-4bef-a861-74e5ea21682e/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/3bbe5911-b17b-4499-a584-4da7755d1b12/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf.jpgTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf.jpgimage/jpeg33178https://repositorio.cuc.edu.co/bitstreams/23f9a2d3-2936-4887-8456-ff19976310ac/downloadc71f292be7c0a627e09cbf035edf691bMD54TEXTTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf.txtTime Series Decomposition using Automatic Learning Techniques for Predictive Models.pdf.txttext/plain23591https://repositorio.cuc.edu.co/bitstreams/65bfa3d0-0ada-460b-a54d-1958368fdff4/download09be015b273788484d74b9c3c025dac6MD5511323/6237oai:repositorio.cuc.edu.co:11323/62372024-09-17 14:18:05.24http://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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