A simple and fast representation space for classifying complex time series

"In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It...

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Autores principales: Zunino, Luciano, Olivares, Felipe, Fernández Bariviera, Aurelio, Rosso, Osvaldo A.
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ANALISIS DE SERIES DE TIEMPO
PROCESOS ESTACIONARIOS
CLASIFICACION
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1762
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Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1762
record_format dspace
spelling I32-R138-123456789-17622022-12-07T13:06:31Z A simple and fast representation space for classifying complex time series Zunino, Luciano Olivares, Felipe Fernández Bariviera, Aurelio Rosso, Osvaldo A. ANALISIS DE SERIES DE TIEMPO PROCESOS ESTACIONARIOS CLASIFICACION "In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease. " 2019-09-19T20:02:16Z 2019-09-19T20:02:16Z 2017-03 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 0375-9601 http://ri.itba.edu.ar/handle/123456789/1762 en info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physleta.2017.01.047 info:eu-repo/grantAgreement/CONICET/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic ANALISIS DE SERIES DE TIEMPO
PROCESOS ESTACIONARIOS
CLASIFICACION
spellingShingle ANALISIS DE SERIES DE TIEMPO
PROCESOS ESTACIONARIOS
CLASIFICACION
Zunino, Luciano
Olivares, Felipe
Fernández Bariviera, Aurelio
Rosso, Osvaldo A.
A simple and fast representation space for classifying complex time series
topic_facet ANALISIS DE SERIES DE TIEMPO
PROCESOS ESTACIONARIOS
CLASIFICACION
description "In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease. "
format Artículos de Publicaciones Periódicas
acceptedVersion
author Zunino, Luciano
Olivares, Felipe
Fernández Bariviera, Aurelio
Rosso, Osvaldo A.
author_facet Zunino, Luciano
Olivares, Felipe
Fernández Bariviera, Aurelio
Rosso, Osvaldo A.
author_sort Zunino, Luciano
title A simple and fast representation space for classifying complex time series
title_short A simple and fast representation space for classifying complex time series
title_full A simple and fast representation space for classifying complex time series
title_fullStr A simple and fast representation space for classifying complex time series
title_full_unstemmed A simple and fast representation space for classifying complex time series
title_sort simple and fast representation space for classifying complex time series
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1762
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