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

Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.

Detalles Bibliográficos
Autores principales: Rodriguez Rivero, Cristian, Pucheta, Julián, Laboret, Sergio, Sauchelli, Victor, Orjuela-Cañon, Alvaro David, Franco, Leonardo
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2024
Materias:
Time series analysis
Entropy
Forecasting
Neural networks
Acceso en línea:http://hdl.handle.net/11086/553637
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
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spelling I10-R141-11086-5536372024-09-13T06:22:51Z Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series Rodriguez Rivero, Cristian Pucheta, Julián Laboret, Sergio Sauchelli, Victor Orjuela-Cañon, Alvaro David Franco, Leonardo Time series analysis Entropy Forecasting Neural networks 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 2024-09-12T11:29:07Z 2024-09-12T11:29:07Z 2016 conferenceObject 978-1-5090-5106-9 http://hdl.handle.net/11086/553637 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ Impreso; Electrónico y/o Digital
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Time series analysis
Entropy
Forecasting
Neural networks
spellingShingle Time series analysis
Entropy
Forecasting
Neural networks
Rodriguez Rivero, Cristian
Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
topic_facet Time series analysis
Entropy
Forecasting
Neural networks
description Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina.
format conferenceObject
author Rodriguez Rivero, Cristian
Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
author_facet Rodriguez Rivero, Cristian
Pucheta, Julián
Laboret, Sergio
Sauchelli, Victor
Orjuela-Cañon, Alvaro David
Franco, Leonardo
author_sort Rodriguez Rivero, Cristian
title Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series
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
publishDate 2024
url http://hdl.handle.net/11086/553637
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