A combined approach for long-term series prediction: Renyi permutation entropy with BEA predictor filter

In order to predict long-term series, a Bayesian enhanced approach (BEA) combining permutation entropy (BEMA) is presented. The motivation of the proposed filter is to predict long-term time series by changing the structure of the predictor filter according to data model selected, then computational...

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Bibliographic Details
Published in2016 IEEE Biennial Congress of Argentina (ARGENCON) pp. 1 - 5
Main Authors Rivero, Cristian Rodriguez, Pucheta, Julian, Patino, Daniel, Laboret, Sergio, Juarez, Gustavo, Sauchelli, Victor
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Bayes methods
Bayesian enhanced modified approach (BEMA)
Benchmark testing
Complexity theory
Entropy
Neural networks
Predictive models
Renyi permutation entropy
Time series analysis
time series forecasting
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Summary:In order to predict long-term series, a Bayesian enhanced approach (BEA) combining permutation entropy (BEMA) is presented. The motivation of the proposed filter is to predict long-term time series by changing the structure of the predictor filter according to data model selected, then computational results are evaluated on high roughness time series selected from benchmark, in which they are compared with recent artificial neural networks (ANN) nonlinear filters such as Bayesian Enhanced approach (BEA) and Bayesian Approach (BA). These results support the applicability of permutation entropy in analyzing the dynamic behavior of chaotic time series for long-term series predictions.
DOI:10.1109/ARGENCON.2016.7585299