In this paper, a hybrid model integrating wavelet decomposition and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, the Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different...

Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods for regression. These models have been successfully applied for time series modelling and prediction. A critical issue for the performance of these models is the choice of the kernel parameters and the...

Bayesian Neural Networks (BNNs) have been shown as useful tools to analyze modeling uncertainty of Neural Networks (NNs). This research focuses on the comparison of two BNNs. The first BNNs (BNN-I) use statistical methods to describe the characteristics of different uncertainty sources (input,...

This article presents an adaptive very short-term wind power prediction scheme that uses an artificial neural network as predictor along with adaptive Bayesian learning and Gaussian process approximation. A set of recent wind speed measurements samples composes the predictor’s inputs. The...

Purpose – Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much...