Many similarity measures used for classification involve the inverse of the group covariance matrices. However, the number of observations available in the training set for each group is, in many cases, significantly inferior to the dimension of the feature space, what implies that the sample covariance matrix is singular. A common solution to this problem is to assume the same covariance matri...

In this paper, we address the problem of ill-conditioning Wiener filter, optimal linear minimum mean square error estimator. Computing filter involves inverse observation covariance matrix. practice, matrix has a large condition number, resulting in unreliable numerical computation filter. To issue, develop four approximate formulas using truncation technique based on principal components compo...

Abstract Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that can describe the dynamic characteristics of financial processes make more accurate forecasts than other machine learning techniques. The first main contribution this paper to propose methodology modelling forecasting...

Summary In this paper, some multivariate and double modelling approaches are presented. Moreover, article provides an overview of the structure covariance matrix. Furthermore, methods identification given.