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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15812
Title: Noisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: Application to rainfall series
Authors: Rodriguez Rivero, Cristian
Pucheta, Julian
Orjuela Canon, Alvaro
Franco, Leonardo
Túpac Valdivia, Yván Jesús
Otano, Paula
Sauchelli, V.
Keywords: Complex networks;Dynamical systems;Enterprise software;Entropy;Forecasting;Neural networks;Time series;White noise;Acceptable performance;Chaotic time series;Chaotic time series forecast;Computational results;Computationally efficient;energy associated to series (EAS);Prediction performance;Renyi's entropic information;Rain
Issue Date: 2017
Publisher: IEEE Computer Society
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022194082&doi=10.1109%2fTLA.2017.7959353&partnerID=40&md5=4ff444292907841eb800f6f39d7a0722
Abstract: This article proposes that the combination of smoothing approach considering the entropic information provided by Renyi's 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 Mackey Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca-Cordoba, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi's entropy of the series. 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 to show the predictability of noisy rainfall and chaotic time series reported in the literature. © 2003-2012 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15812
ISSN: 15480992
Appears in Collections:Artículos de investigación

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