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dc.creatorsilva d, jesus g
dc.creatorJesús Vargas Villa
dc.creatorCabrera, Danelys
dc.date.accessioned2019-07-31T22:45:24Z
dc.date.available2019-07-31T22:45:24Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11323/5129
dc.description.abstractThe objective of this paper is to carry out the comparison and selection of a method to forecast sales of basic food products efficiently. The source of data comes from a set of popular markets in the main departments of Colombia. The methods and methodologies used are: Hold Method, Winters, the Box Jenkins methodology (ARIMA) and an Artificial Neural Network. The results show that the artificial neural network obtained a better performance achieving the lowest mean square error.es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de la Costaes_ES
dc.relation.ispartof10.1007/978-3-030-23887-2_5es_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectArtificial Neural Networks (ANN)es_ES
dc.subjectCommoditieses_ES
dc.subjectSales forecastes_ES
dc.titleSale forecast for basic commodities based on artificial neural networks predictiones_ES
dc.typeConference paperes_ES
dc.type.hasVersioninfo:eu-repo/semantics/draftes_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal