Browsing by Author "Franco, Leonardo"

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    Noisy Chaotic time series forecast approximated by combining Reny's entropy with Energy associated to series method: Application to rainfall series
    (IEEE Computer Society, 2017) Rodriguez Rivero, Cristian; Pucheta, Julian; Orjuela Canon, Alvaro; Franco, Leonardo; Túpac Valdivia, Yván Jesús; Otano, Paula; Sauchelli, V.
    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.
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    Time-series prediction with BEMCA approach: Application to short rainfall series
    (Institute of Electrical and Electronics Engineers Inc., 2018) Rodriguez Rivero, Cristian; Túpac Valdivia, Yván Jesús; Pucheta, Julian; Juarez, Gustavo; Franco, Leonardo; Otaño, Paula
    This paper presents a new method to forecast short rainfall time-series. The new framework is by means of Bayesian enhanced modified combined approach (BEMCA) using permutation and relative entropy with Bayesian inference. The aim at the proposed filter is focused on short datasets consisting of at least 36 samples. The structure of the artificial neural networks (ANNs) change according to data model selected, such as the Bayesian approach can be combined with the entropic information of the series. Then computational results are assessed on time series competition and rainfall series, afterwards they are compared with ANN nonlinear approaches proposed in recent work and naïve linear technique such us ARMA. To show a better performance of BEMCA filter, results are analyzed in their forecast horizons by SMAPE and RMSE indices. BEMCA filter shows an increase of accuracy in 3-6 prediction horizon analyzing the dynamic behavior of chaotic series for short series predictions. © 2017 IEEE.