This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to ...

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. ...

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 ...

In the last two decades, with the rise of the Data Mining process, there is an increasing interest in the adaptation of Machine Learning methods to support Time Series non-parametric modeling and prediction. The non-parametric ...

In this paper, wind power series prediction using BEA modified (BEAmod.) neural networks-based approach is presented. Wind power forecasting is a complex, multidimensional, and highly non-linear system. Neural network is ...

In this work, a method based on information theory is developed to make predictions from a sample of nonlinear time series data. Numerical examples are given to illustrate the effectiveness of the proposed method.

Two stage grid search accepted as a promising heuristic search technique, involves a search performed in two stages. In the first stage a search is performed in coarse grain/low resolution to identify the optimal region ...

Segment Growing Neural Gas (Segment-GNG) has been recently proposed as a new spatiotemporal quantization method for time series. Unlike traditional quantization algorithms that are prototype-based, Segment-GNG uses segments ...