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dc.creatorSilva, Jesús
dc.creatorVarela, Noel
dc.creatorMartínez Caraballo, Hugo
dc.creatorGarcía Guiliany, Jesús
dc.creatorCabas Vásquez, Luis Carlos
dc.creatorNavarro Beltrán, Jorge
dc.creatorLeón Castro, Nadia
dc.date.accessioned2019-08-08T14:42:39Z
dc.date.available2019-08-08T14:42:39Z
dc.date.issued2019-06-26
dc.identifier.isbn978-3-030-22795-1
dc.identifier.isbn978-3-030-22796-8
dc.identifier.urihttp://hdl.handle.net/11323/5133
dc.description.abstractThe prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive 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 basic agricultural products, considering seasonal factors.es_ES
dc.language.isoenges_ES
dc.publisherInternational Symposium on Neural Networkses_ES
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-22796-8_38es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectForecastes_ES
dc.subjectMultiple Input Multiple Outputes_ES
dc.subjectMultilayer perceptrones_ES
dc.subjectPredictive modeles_ES
dc.subjectCyclic variationes_ES
dc.subjectSupport vector machineses_ES
dc.titleAn Early Warning Method for Basic Commodities Price Based on Artificial Neural Networkses_ES
dc.typePreprintes_ES
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