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...

Full description

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
id RCUC2_d469326458d5d388eb94bf9c2ed0cdf8
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6471
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
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
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spelling 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 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