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dc.contributor.advisorQuintero Monroy, Christian Giovanny
dc.contributor.authorJiménez Mares, Jamer René
dc.date.accessioned2022-08-08T20:51:25Z
dc.date.available2022-08-08T20:51:25Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/10584/10818
dc.description.abstractTime series processes are important in several sectors like marketing, transport, energy, telecommunications, etc. Time series forecasting tasks can help in operative and strategic tasks. Several conventional and non-conventional techniques as ARIMA models, artificial neural networks (ANN), support vector machines (SVM), Regression Tree Ensembles (RTE) or combinations of them have been used for time series modeling. The implementation of this type of techniques provides support in time series modeling, however, normally the trained models may lose performance due to the dynamic behavior of the phenomena. A methodology capable to assess the performance and maintenance of the models is necessary to guarantee the automatic adaptability in each case. Hence, in this research an adaptive methodology based on computational intelligence for time series modeling is proposed. In this case, an Auditor is developed, which allows identifying when a model must be retrained or updated before losing forecast performance. Furthermore, when the retrain process is not achieving a better performance, a new metric is proposed to choose which time series modeling technique is included in the knowledge base. The intelligent system allows building the time series model automatically, considering exogenous variables such as weather, calendar and statistical transformations to group and simplify the number of models required.
dc.formatapplication/pdfes_ES
dc.format.extent129 páginases_ES
dc.language.isoenges_ES
dc.publisherUniversidad del Nortees_ES
dc.titleAdaptive methodology based on computational intelligence for time series modelinges_ES
dc.typeTrabajo de grado - Doctoradoes_ES
dc.publisher.programDoctorado en Ingeniería Eléctrica y Electrónicaes_ES
dc.publisher.departmentDepartamento de eléctrica y electrónicaes_ES
dc.description.degreelevelDoctoradoes_ES
dc.publisher.placeBarranquilla, Colombiaes_ES
dc.rights.creativecommonshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_db06es_ES
dc.type.driverinfo:eu-repo/semantics/doctoralThesises_ES
dc.type.contentTextes_ES
dc.type.versioninfo:eu-repo/semantics/updatedVersiones_ES
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaes_ES
dc.description.degreenameDoctor en Ingeniería Eléctrica y Electrónicaes_ES
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2es_ES
dcterms.audience.educationalcontextEstudianteses_ES
dc.subject.lembInteligencia computacional
dc.subject.lembAnálisis de series de tiempo
dcterms.audience.professionaldevelopmentDoctoradoes_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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