An early warning method for agricultural products price spike based on artificial neural networks prediction
In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differenti...
- Autores:
-
Silva, Jesús
Gaitán-Angulo, Mercedes
Romero Borré, Jenny
Lozano Ayarza, Liliana Patricia
Pineda Lezama, Omar Bonerge
Martínez Galán, Zuleima del Carmen
Navarro Beltran, Jorge
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6471
- Acceso en línea:
- https://hdl.handle.net/11323/6471
https://repositorio.cuc.edu.co/
- Palabra clave:
- Predictive model
Multilayer perceptron
Multiple input multiple output
Forecast
Support vector machines
Cyclic variation
- Rights
- openAccess
- License
- CC0 1.0 Universal
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oai:repositorio.cuc.edu.co:11323/6471 |
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|
dc.title.spa.fl_str_mv |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
title |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
spellingShingle |
An early warning method for agricultural products price spike based on artificial neural networks prediction Predictive model Multilayer perceptron Multiple input multiple output Forecast Support vector machines Cyclic variation |
title_short |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
title_full |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
title_fullStr |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
title_full_unstemmed |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
title_sort |
An early warning method for agricultural products price spike based on artificial neural networks prediction |
dc.creator.fl_str_mv |
Silva, Jesús Gaitán-Angulo, Mercedes Romero Borré, Jenny Lozano Ayarza, Liliana Patricia Pineda Lezama, Omar Bonerge Martínez Galán, Zuleima del Carmen Navarro Beltran, Jorge |
dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Gaitán-Angulo, Mercedes Romero Borré, Jenny Lozano Ayarza, Liliana Patricia Pineda Lezama, Omar Bonerge Martínez Galán, Zuleima del Carmen Navarro Beltran, Jorge |
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 |
In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of 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 agricultural products, considering macro and micro economic variables and factors related to the seasons of the year. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2020-07-07T19:11:53Z |
dc.date.available.none.fl_str_mv |
2020-07-07T19:11:53Z |
dc.date.issued.none.fl_str_mv |
2020 |
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 |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ARTOTR |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_816b |
status_str |
acceptedVersion |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/6471 |
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/ |
url |
https://hdl.handle.net/11323/6471 https://repositorio.cuc.edu.co/ |
identifier_str_mv |
Corporación Universidad de la Costa REDICUC - Repositorio CUC |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Universidad de la Costa |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
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Silva, JesúsGaitán-Angulo, MercedesRomero Borré, JennyLozano Ayarza, Liliana PatriciaPineda Lezama, Omar BonergeMartínez Galán, Zuleima del CarmenNavarro Beltran, Jorge2020-07-07T19:11:53Z2020-07-07T19:11:53Z2020https://hdl.handle.net/11323/6471Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of 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 agricultural products, considering macro and micro economic variables and factors related to the seasons of the year.Silva, JesúsGaitán-Angulo, Mercedes-will be generated-orcid-0000-0002-8248-8788-600Romero Borré, JennyLozano Ayarza, Liliana Patricia-will be generated-orcid-0000-0001-5186-2864-600Pineda Lezama, Omar BonergeMartínez Galán, Zuleima del CarmenNavarro Beltran, JorgeengUniversidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Predictive modelMultilayer perceptronMultiple input multiple outputForecastSupport vector machinesCyclic variationAn early warning method for agricultural products price spike based on artificial neural networks predictionPre-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 Agricultural Products Price Spike Based on Artificial Neural Networks Prediction.pdfAn Early Warning Method for Agricultural Products Price Spike Based on Artificial Neural Networks Prediction.pdfapplication/pdf179567https://repositorio.cuc.edu.co/bitstreams/5665ca6a-2bea-4530-87c4-a174ce82b3e2/download0110f3f0d84f4e7ee4e87d5c078b6610MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/1746f371-467f-49b8-8c65-5e406db85daf/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/179c4b9e-a021-499c-9e78-590be6423ea4/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILAn Early Warning Method for Agricultural Products 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|