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.
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