Genetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholds

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dc.contributor.advisorSuárez Sierra, Biviana Marcelaspa
dc.contributor.authorTaimal Yepes, Carlos Alberto
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.degreeMagíster en Matemáticas Aplicadasspa
dc.creator.emailcataimaly@eafit.edu.cospa
dc.date.accessioned2024-01-30T23:31:08Z
dc.date.available2024-01-30T23:31:08Z
dc.date.issued2023
dc.description.abstractAlthough the applications of Non-Homogeneous Poisson Processes (NHPP) to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an eficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model it poorly in regards of the information contained in the real one. Because of this, a new method is proposed to solve the segmentation uncertainty of the time series of measurements, where the generating distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that trespassed are candidates to be a change-point, so all the possible configurations of overflow days under the heuristics of a genetic algorithm are the possible chromosomes, which will unite to produce new solutions. Also, such methods will be guarantee to non-local and the best possible one solution, reducing wasted machine time evaluating the least likely chromosomes to be a feasible solution. The analytical evaluation technique will be by means of the Minimum Description Length (MDL) as the objective function, which is the joint posterior distribution function of the parameters of the NHPP of each regime and the change-points that determines them and which account as well for the influence of the presence of said times. Thus, one of the practical implications of the present work comes in terms of overcoming the need of modeling the time series of measurements, where the distributions of exceedances of certain thresholds, or where the counting of certain events involving abrupt changes, is the main focus with applications in phenomena such as climate change, information security and epidemiology, to name a few.spa
dc.identifier.ddc519.625 T133
dc.identifier.urihttps://hdl.handle.net/10784/33242
dc.language.isospaspa
dc.publisherUniversidad EAFITspa
dc.publisher.departmentEscuela de Ciencias Aplicadas e Ingeniería. Departamento de Ciencias Matemáticasspa
dc.publisher.placeMedellínspa
dc.publisher.programMaestría en Matemáticas Aplicadasspa
dc.rightsTodos los derechos reservadosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subjectLongitud Mínima de Descripción
dc.subjectProcesos de poisson no homogéneos
dc.subjectDetección de múltiples puntos de cambio
dc.subjectDistribución máxima A Posteriori
dc.subjectAlgoritmos Genéticos
dc.subject.keywordMultiple Change-point Detection
dc.subject.keywordGenetic Algorithms
dc.subject.keywordMinimum Description Length
dc.subject.keywordNon-homogeneous Poisson Processes
dc.subject.keywordMaximum A Posteriori Estimation
dc.subject.lembALGORITMOS GENÉTICOS
dc.subject.lembPROGRAMACIÓN GENÉTICA (CIENCIA DE LA COMPUTACIÓN)
dc.titleGenetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholdsspa
dc.typemasterThesiseng
dc.typeinfo:eu-repo/semantics/masterThesiseng
dc.type.hasVersionacceptedVersioneng
dc.type.localTesis de Maestríaspa
dc.type.spaArtículospa

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