title: Noise model based ν-support vector regression with its application to short-term wind speed forecasting.
abstract: Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique.
authors.affiliation: College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei, 050024, China; School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China. Electronic address: huqinghua@tju.edu.cn.
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Hu
authors.affiliation: College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei, 050024, China; College of Mathematics and Computer Science, Hengshui University, Hengshui, Hebei, 053000, China.
authors.forename: Shiguang
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Computer Software, Tianjin University, Tianjin, 300072, China.
authors.forename: Zongxia
authors.initials: Z
authors.lastname: Xie
authors.affiliation: College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei, 050024, China; Hebei Key Laboratory of Computational Mathematics and Applications, Shijiazhuang, Hebei, 050024, China.
authors.forename: Jusheng
authors.initials: J
authors.lastname: Mi
authors.affiliation: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wan
mesh_terms: D001499:Bayes Theorem; D055904:Meteorology / Q000379:methods; D008962:Models, Theoretical; D016011:Normal Distribution; D012044:Regression Analysis; D060388:Support Vector Machine; D014919:Wind
keywords: Inequality constraints; Loss function; Noise model; Support vector regression; Wind speed forecasting