Characterization of autoregressive processes using entropic quantifiers

Autores
Traversaro Varela, Francisco; Redelico, Francisco Oscar
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.
Fil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Hospital Italiano; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina
Materia
PERMUTATION ENTROPY
TIME SERIES ANALYSIS
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:11336/93004
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Characterization of autoregressive processes using entropic quantifiersTraversaro Varela, FranciscoRedelico, Francisco OscarPERMUTATION ENTROPYTIME SERIES ANALYSIShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.Fil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Hospital Italiano; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaElsevier Science2018-01-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/93004Traversaro Varela, Francisco; Redelico, Francisco Oscar; Characterization of autoregressive processes using entropic quantifiers; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 490; 15-1-2018; 13-230378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S0378437117307136info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2017.07.025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:49Zoai:ri.conicet.gov.ar:11336/93004instacron: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:34982025-09-03 09:45:49.849CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Characterization of autoregressive processes using entropic quantifiers
title Characterization of autoregressive processes using entropic quantifiers
spellingShingle Characterization of autoregressive processes using entropic quantifiers
Traversaro Varela, Francisco
PERMUTATION ENTROPY
TIME SERIES ANALYSIS
title_short Characterization of autoregressive processes using entropic quantifiers
title_full Characterization of autoregressive processes using entropic quantifiers
title_fullStr Characterization of autoregressive processes using entropic quantifiers
title_full_unstemmed Characterization of autoregressive processes using entropic quantifiers
title_sort Characterization of autoregressive processes using entropic quantifiers
dc.creator.none.fl_str_mv Traversaro Varela, Francisco
Redelico, Francisco Oscar
author Traversaro Varela, Francisco
author_facet Traversaro Varela, Francisco
Redelico, Francisco Oscar
author_role author
author2 Redelico, Francisco Oscar
author2_role author
dc.subject.none.fl_str_mv PERMUTATION ENTROPY
TIME SERIES ANALYSIS
topic PERMUTATION ENTROPY
TIME SERIES ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.
Fil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Hospital Italiano; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina
description The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-15
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/11336/93004
Traversaro Varela, Francisco; Redelico, Francisco Oscar; Characterization of autoregressive processes using entropic quantifiers; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 490; 15-1-2018; 13-23
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/93004
identifier_str_mv Traversaro Varela, Francisco; Redelico, Francisco Oscar; Characterization of autoregressive processes using entropic quantifiers; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 490; 15-1-2018; 13-23
0378-4371
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://linkinghub.elsevier.com/retrieve/pii/S0378437117307136
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2017.07.025
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
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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
_version_ 1842268755686588416
score 12.885934

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