Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection

Autores
Martos, Gabriel Alejandro; Hernández, Nicolás; Muñoz, Alberto; Moguerza, Javier
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
Fil: Martos, Gabriel Alejandro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hernández, Nicolás. Universidad Carlos III de Madrid. Instituto de Salud; España
Fil: Muñoz, Alberto. Universidad Carlos III de Madrid. Instituto de Salud; España
Fil: Moguerza, Javier. Universidad Rey Juan Carlos; España
Materia
ANOMALY DETECTION
ENTROPY
FUNCTIONAL DATA
MINIMUM-ENTROPY SETS
STOCHASTIC PROCESS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/92560
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Entropy Measures for Stochastic Processes with Applications in Functional Anomaly DetectionMartos, Gabriel AlejandroHernández, NicolásMuñoz, AlbertoMoguerza, JavierANOMALY DETECTIONENTROPYFUNCTIONAL DATAMINIMUM-ENTROPY SETSSTOCHASTIC PROCESShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.Fil: Martos, Gabriel Alejandro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hernández, Nicolás. Universidad Carlos III de Madrid. Instituto de Salud; EspañaFil: Muñoz, Alberto. Universidad Carlos III de Madrid. Instituto de Salud; EspañaFil: Moguerza, Javier. Universidad Rey Juan Carlos; EspañaMolecular Diversity Preservation International2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/92560Martos, Gabriel Alejandro; Hernández, Nicolás; Muñoz, Alberto; Moguerza, Javier; Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection; Molecular Diversity Preservation International; Entropy; 20; 1; 1-20181099-4300CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1099-4300/20/1/33info:eu-repo/semantics/altIdentifier/doi/10.3390/e20010033info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:52:07Zoai:ri.conicet.gov.ar:11336/92560instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:52:08.245CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
title Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
spellingShingle Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
Martos, Gabriel Alejandro
ANOMALY DETECTION
ENTROPY
FUNCTIONAL DATA
MINIMUM-ENTROPY SETS
STOCHASTIC PROCESS
title_short Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
title_full Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
title_fullStr Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
title_full_unstemmed Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
title_sort Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
dc.creator.none.fl_str_mv Martos, Gabriel Alejandro
Hernández, Nicolás
Muñoz, Alberto
Moguerza, Javier
author Martos, Gabriel Alejandro
author_facet Martos, Gabriel Alejandro
Hernández, Nicolás
Muñoz, Alberto
Moguerza, Javier
author_role author
author2 Hernández, Nicolás
Muñoz, Alberto
Moguerza, Javier
author2_role author
author
author
dc.subject.none.fl_str_mv ANOMALY DETECTION
ENTROPY
FUNCTIONAL DATA
MINIMUM-ENTROPY SETS
STOCHASTIC PROCESS
topic ANOMALY DETECTION
ENTROPY
FUNCTIONAL DATA
MINIMUM-ENTROPY SETS
STOCHASTIC PROCESS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
Fil: Martos, Gabriel Alejandro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hernández, Nicolás. Universidad Carlos III de Madrid. Instituto de Salud; España
Fil: Muñoz, Alberto. Universidad Carlos III de Madrid. Instituto de Salud; España
Fil: Moguerza, Javier. Universidad Rey Juan Carlos; España
description We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/92560
Martos, Gabriel Alejandro; Hernández, Nicolás; Muñoz, Alberto; Moguerza, Javier; Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection; Molecular Diversity Preservation International; Entropy; 20; 1; 1-2018
1099-4300
CONICET Digital
CONICET
url http://hdl.handle.net/11336/92560
identifier_str_mv Martos, Gabriel Alejandro; Hernández, Nicolás; Muñoz, Alberto; Moguerza, Javier; Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection; Molecular Diversity Preservation International; Entropy; 20; 1; 1-2018
1099-4300
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1099-4300/20/1/33
info:eu-repo/semantics/altIdentifier/doi/10.3390/e20010033
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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