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dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.authorValencia-Cárdenas, Marisol
dc.contributor.authorCorrea-Morales, Juan Carlos
dc.date.accessioned2020-02-07T13:55:53Z
dc.date.available2020-02-07T13:55:53Z
dc.date.issued2020-01-30
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/75559
dc.description.abstractActualmente existen diversas metodologías de pronóstico, que van desde el conocimiento empírico hasta métodos innovadores, individuales o combinados, que demuestran resultados óptimos. Este documento se deriva de un proceso de investigación y presenta alternativas relacionadas con las combinaciones de pronósticos, utilizando metaheurísticas, por ejemplo, mediante la búsqueda tabú y la programación evolutiva para optimizar el pronóstico. El documento presenta pronósticos combinados basados en la programación evolutiva utilizando mezclas de modelos de regresión bayesiana y modelos de regresión lineal clásico, el modelo de media móvil integrado autorregresivo, el suavizado exponencial y la regresión bayesiana. El documento presenta dos artículos derivados de investigación, la primera compara el algoritmo combinado con los resultados individuales de estos modelos individuales y con la combinación de Bates y Granger utilizando un indicador de error y el valor simétrico de error absoluto medio. Esos modelos y la combinación se aplicaron a la simulación de series temporales y a un caso real de ventas de productos lácteos, generando así pronósticos combinados multiproductos tanto para la simulación como para el caso real. La nueva combinación combinada con la metaheurística evolutiva mostró mejores resultados que los de los otros que se utilizaron. La segunda investigación utiliza series de tiempo simuladas, diseñando dos metaheurísticas basadas en la lista Tabú, que aprenden de los datos con base en el comportamiento estadístico de éstos, como el cluster, así como del mismo valor optimizado del error de ajuste, y se comparan las combinaciones de pronósticos con resultados de modelos individuales a tres tipos de series de tiempo.
dc.description.abstractCurrently diverse forecasting methodologies exists, going from the empirical knowledge to the innovative methods, individual or combined, demonstrating optimal results. This document is derived from a research process, and presents alternatives related to forecast combinations, using metaheuristics, for example, by using Tabu search and Evolutive programing to optimize forecasting. One of the designed process consists of creating combination forecasts based on evolutionary programming using, first, a mixture of Bayesian regression models and, second, a mixture of the classical linear regression model, the autoregressive integrated moving average model, exponential smoothing and Bayesian regression. The first research compares the novel combined algorithm with the individual results of these individual models and with the Bates and Granger combination using an error indicator and the symmetrical mean absolute error value. Those models and the novel design were applied to time series simulation and to a real case of dairy products sales, thus generating multiproduct combination forecasts for both the simulation and the real case. The novel combination combined with the evolutionary metaheuristic showed better results than those of the others that were used. The second research uses simulated time series and other metaheuristic that learns from the data an statistical behavior.
dc.description.sponsorshipTecnológico de Antioquia, Universidad Nacional de Colombia.
dc.format.extent41 páginas
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddcMatemáticas::Probabilidades y matemáticas aplicadas
dc.titleForecast combination using Optimization techniques
dc.title.alternativeForecast Combination
dc.typeLibro
dc.rights.spaAcceso abierto
dc.coverage.sucursalUniversidad Nacional de Colombia - Sede Medellín
dc.type.driverinfo:eu-repo/semantics/book
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.description.degreelevelDoctorado
dc.publisher.departmentEscuela de estadística
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellín
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalBúsqueda tabú
dc.subject.proposalMetaheuristics
dc.subject.proposalForecast Combination
dc.subject.proposalPronostico
dc.subject.proposalProgramación evolutiva
dc.subject.proposalRegresión bayesiana
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/LIB
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial-SinDerivadas 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit