Title: Short time series prediction: Bayesian enhanced modified approach with application to cumulative rainfall series

Authors: Cristian Rodriguez Rivero; Julian Antonio Pucheta; Victor Hugo Sauchelli; Hector Daniel Patiño

Addresses: Electronic Engineering Department, Universidad Nacional de Córdoba (UNC), Vélez Sarsfield 1611, X5016GCA Córdoba, Argentina ' Electronic Engineering Department, Universidad Nacional de Córdoba (UNC), Vélez Sarsfield 1611, X5016GCA Córdoba, Argentina ' Electronic Engineering Department, Universidad Nacional de Córdoba (UNC), Vélez Sarsfield 1611, X5016GCA Córdoba, Argentina ' Institute of Automatic (INAUT), National University of San Juan, San Martín 1109 (oeste), 5400 San Juan, Argentina

Abstract: This article contributes with short time series prediction with complete and incomplete datasets based on a new framework by means of Bayesian enhanced modified approach (BEMA) combining permutation entropy. The focus of the proposed filter with particularly interest in incomplete datasets or missing data is by changing the structure of the predictor filter according to data model selected, in which the Bayesian approach can be combined with entropic information of the series. The simplest method adopted to imputing the missing data on the dataset is by linear average smoothing, then computational results are evaluated on high roughness time series selected from benchmark series, in which they are compared with artificial neural networks (ANN) nonlinear filters such as Bayesian enhanced approach (BEA) and Bayesian approach (BA) proposed in recent work, in order to show a better performance of BEMA filter. These results support the applicability of permutation entropy in analysing the dynamic behaviour of chaotic time series for short series predictions.

Keywords: cumulative rainfall series; forecasting; permutation entropy; Bayesian enhanced modified approach; BEMA filter; complete datasets; incomplete datasets; short time series; precipitation; missing data; predictor filter; chaotic time series.

DOI: 10.1504/IJICA.2016.078730

International Journal of Innovative Computing and Applications, 2016 Vol.7 No.3, pp.153 - 162

Received: 11 Mar 2016
Accepted: 16 Mar 2016
Published online: 01 Sep 2016 *

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