Noise model based \(\nu\)-support vector regression with its application to short-term wind speed forecasting

DOI10.1016/j.neunet.2014.05.003zbMath1325.62177OpenAlexW2096290418WikidataQ39184800 ScholiaQ39184800MaRDI QIDQ889277

Zongxia Xie, Shiguang Zhang, Jie Wan, Qinghua Hu, Ju-Sheng Mi

Publication date: 6 November 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2014.05.003

zbMATH Keywords

loss functioninequality constraintsnoise modelwind speed forecastingsupport vector regression

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Learning and adaptive systems in artificial intelligence (68T05) Meteorology and atmospheric physics (86A10)

Related Items (3)

Robust manifold broad learning system for large-scale noisy chaotic time series prediction: a perturbation perspective ⋮ Robust extreme learning machine for modeling with unknown noise ⋮ Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function

Uses Software

• ElemStatLearn
• PRMLT

Cites Work

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• Support-vector networks
• The multiplier method of Hestenes and Powell applied to convex programming
• Training v-Support Vector Regression: Theory and Algorithms
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Robust Estimation of a Location Parameter
• Augmented Lagrange Multiplier Functions and Duality in Nonconvex Programming
• Robust Statistics

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