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...

Most works on Support Vector Regression (SVR) focus kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance datasets. However, fixed radius limitation prevents adaptive selection of according to data distribution characteristics, compromising SVR-based methods. Therefore, this study proposes a...

This paper studies the addition of linear constraints to Support Vector Regression when kernel is linear. Adding those into problem allows add prior knowledge on estimator obtained, such as finding positive vector, probability vector or monotone data. We prove that related optimization stays a semi-definite quadratic problem. also propose generalization Sequential Minimal Optimization algorithm...

Support Vector Regression (SVR) is usually pursued using the 2–insensitive loss function while, alternatively, the initial regression problem can be reduced to a properly defined classification one. In either case, slack variables have to be introduced in practical interesting problems, the usual choice being the consideration of linear penalties for them. In this work we shall discuss the solu...

Support vector regression builds a model of a process that depends on a set of factors. It traditionally considers one output at a time, which means that advantage cannot be taken of the correlations that may exist between outputs. The purpose of this paper is to show how the body of knowledge accumulated by geostatisticians on Kriging and its extensions over the last 40 years can help extend s...