Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series

Autores
Rodriguez Rivero, Cristian; Pucheta, Julián; Laboret, Sergio; Sauchelli, Victor; Orjuela-Cañon, Alvaro David; Franco, Leonardo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.
Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.
This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi’ method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature.
http://ieeexplore.ieee.org/document/7885702/
Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.
Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.
Control Automático y Robótica
Materia
Time series analysis
Entropy
Forecasting
Neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/553637
Acceder
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oai_identifier_str oai:rdu.unc.edu.ar:11086/553637
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall seriesRodriguez Rivero, CristianPucheta, JuliánLaboret, SergioSauchelli, VictorOrjuela-Cañon, Alvaro DavidFranco, LeonardoTime series analysisEntropyForecastingNeural networksFil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi’ method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature.http://ieeexplore.ieee.org/document/7885702/Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.Control Automático y Robótica2016info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf978-1-5090-5106-9http://hdl.handle.net/11086/553637enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-04T12:31:51Zoai:rdu.unc.edu.ar:11086/553637Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-04 12:31:51.258Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
title Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
spellingShingle Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
Rodriguez Rivero, Cristian
Time series analysis
Entropy
Forecasting
Neural networks
title_short Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
title_full Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
title_fullStr Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
title_full_unstemmed Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
title_sort Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
dc.creator.none.fl_str_mv Rodriguez Rivero, Cristian
Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
author Rodriguez Rivero, Cristian
author_facet Rodriguez Rivero, Cristian
Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
author_role author
author2 Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Time series analysis
Entropy
Forecasting
Neural networks
topic Time series analysis
Entropy
Forecasting
Neural networks
dc.description.none.fl_txt_mv Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.
Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.
This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi’ method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature.
http://ieeexplore.ieee.org/document/7885702/
Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Sauchelli, Victor. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
Fil: Orjuela-Cañon, Alvaro David. Universidad Antonio Nariño Bogotá. Ingeniería Electrónica y Biomédica; Colombia.
Fil: Franco, Leonardo. Universidad de Málaga. Departamento de Informática; España.
Control Automático y Robótica
description Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv 978-1-5090-5106-9
http://hdl.handle.net/11086/553637
identifier_str_mv 978-1-5090-5106-9
url http://hdl.handle.net/11086/553637
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Digital Universitario (UNC)
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instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 13.13397

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