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.
| Main Authors: | , , , , , |
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| Format: | conferenceObject |
| Language: | eng |
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2024
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| Online Access: | http://hdl.handle.net/11086/553637 |
| _version_ | 1811173560834588672 |
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| 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 |
| collection | Repositorio Digital Universitario |
| description | Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de Ingeniería Electrónica; Argentina. |
| format | conferenceObject |
| id | rdu-unc.553637 |
| institution | Universidad Nacional de Cordoba |
| language | eng |
| publishDate | 2024 |
| record_format | dspace |
| spelling | rdu-unc.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 |
| 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 |
| title | 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_short | 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 |
| topic | Time series analysis Entropy Forecasting Neural networks |
| url | http://hdl.handle.net/11086/553637 |
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