BENCHMARKING AMONG ARTIFICIAL INTELLIGENCE TECHNIQUES APPLIED TO FORECAST

dc.contributor.authorIzquierdo Ortiz, Cristhian Johnatanspa
dc.contributor.authorMontenegro Marin, Carlos Enriquespa
dc.date2015-12-28
dc.date.accessioned2019-09-19T21:53:34Z
dc.date.available2019-09-19T21:53:34Z
dc.descriptionThe article is about creating a space for multiple tests of demand forecasting techniques, this space is a software development where besides to testing the algorithms on the same database, these code routines can be compared with each other, this tool allows generate forecasts to be usable in decision making on purchases of Distribution Companies. Besides comparing forecasting some simple techniques like Moving Average (MM) and Last Period with other techniques such as Artificial Neural Networks (ARN) and genetic algorithms (GA), the comparison is made taking into account the error criteria of generated forecasts and the processing time of the methods. Throughout the article explains the design, development and implementation of the above methods and their integration with the tool.es-ES
dc.formatapplication/pdf
dc.identifierhttps://revistas.udistrital.edu.co/index.php/visele/article/view/9872
dc.identifier10.14483/22484728.9872
dc.identifier.urihttp://hdl.handle.net/11349/21866
dc.languagespa
dc.publisherUniversidad Distrital Francisco José de Caldases-ES
dc.relationhttps://revistas.udistrital.edu.co/index.php/visele/article/view/9872/10982
dc.rightsDerechos de autor 2015 Visión Electrónica: algo más que un estado sólidoes-ES
dc.sourceVisión electrónica; Vol 8 No 2 (2014); 55-66en-US
dc.sourceVisión electrónica; Vol. 8 Núm. 2 (2014); 55-66es-ES
dc.source2248-4728
dc.source1909-9746
dc.subjectDemand forecastinges-ES
dc.subjectGenetic algorithmses-ES
dc.subjectArtificial neural networkses-ES
dc.subjectForecasting methodses-ES
dc.titleBENCHMARKING AMONG ARTIFICIAL INTELLIGENCE TECHNIQUES APPLIED TO FORECASTes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.type.coarhttp://purl.org/coar/resource_type/c_6501

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