Twin proximal support vector regression with heteroscedastic Gaussian noise.

Twin proximal support vector regression, a novel approach, merges twin support vector regression (TSVR) with proximal support vector regression (PSVR) for regression analysis. Classical PSVR and TSVR assume that the data noise follows a homoscedastic Gaussian distribution with zero mean. However, in the real world, such as short-term wind speed prediction and wind farm power generation prediction, noise tends to follow a heteroscedastic Gaussian distribution with zero mean. Therefore, based on the aforementioned model framework and added the heteroscedastic noise feature, we develop a novel regression model named the twin proximal support vector regression model with heteroscedastic Gaussian noise (TPSVR-HGN). The Augment Lagrange multiplier approach is used to solve the proposed TPSVR-HGN model. On the artificial dataset and the real world dataset, compared with recent advanced models, the prediction accuracy of the TPSVR-HGN model has been improved by about 7% and 11% respectively. As the results of this experiment indicate, our method is effective and feasible. • TPSVR-HGN model proposed for heteroscedastic Gaussian noise. • Adding bias term broadens kernel function selection range in TPSVR-HGN. • TPSVR-HGN outperforms existing methods across diverse experimental datasets. [ABSTRACT FROM AUTHOR]