Entropy measures for stochastic processes with applications in functional anomaly detection

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 rel...

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Autores principales: Martos, G., Hernández, N., Muñoz, A., Moguerza, J.M.
Formato: JOUR
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Acceso en línea:http://hdl.handle.net/20.500.12110/paper_10994300_v20_n1_p_Martos
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spelling todo:paper_10994300_v20_n1_p_Martos2023-10-03T16:06:34Z Entropy measures for stochastic processes with applications in functional anomaly detection Martos, G. Hernández, N. Muñoz, A. Moguerza, J.M. Anomaly detection Entropy Functional data Minimum-entropy sets Stochastic process 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. © 2018 by the authors. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10994300_v20_n1_p_Martos
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Anomaly detection
Entropy
Functional data
Minimum-entropy sets
Stochastic process
spellingShingle Anomaly detection
Entropy
Functional data
Minimum-entropy sets
Stochastic process
Martos, G.
Hernández, N.
Muñoz, A.
Moguerza, J.M.
Entropy measures for stochastic processes with applications in functional anomaly detection
topic_facet Anomaly detection
Entropy
Functional data
Minimum-entropy sets
Stochastic process
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. © 2018 by the authors.
format JOUR
author Martos, G.
Hernández, N.
Muñoz, A.
Moguerza, J.M.
author_facet Martos, G.
Hernández, N.
Muñoz, A.
Moguerza, J.M.
author_sort Martos, G.
title Entropy measures for stochastic processes with applications in functional anomaly detection
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
url http://hdl.handle.net/20.500.12110/paper_10994300_v20_n1_p_Martos
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AT hernandezn entropymeasuresforstochasticprocesseswithapplicationsinfunctionalanomalydetection
AT munoza entropymeasuresforstochasticprocesseswithapplicationsinfunctionalanomalydetection
AT moguerzajm entropymeasuresforstochasticprocesseswithapplicationsinfunctionalanomalydetection
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