Noise model based ν-support vector regression with its application to short-term wind speed forecasting.

Area of Science:

• Machine Learning
• Statistical Modeling
• Data Science

Background:

• Support Vector Regression (SVR) typically assumes Gaussian error distributions.
• Real-world data, such as in wind power forecasting, often exhibits non-Gaussian noise (e.g., beta, Laplacian).
• Existing SVR methods are suboptimal when noise deviates from Gaussian assumptions.

Purpose of the Study:

• To develop a generalized ν-support vector regression (N-SVR) technique adaptable to various noise models.
• To derive a robust loss function based on a Bayesian approach for diverse error distributions.
• To enhance regression performance in scenarios with non-Gaussian noise.

Main Methods:

• Derived a general loss function using a Bayesian framework.
• Developed the uniform model of ν-support vector regression for general noise models (N-SVR).
• Employed the Augmented Lagrange Multiplier method for solving the N-SVR optimization problem.

Main Results:

• Demonstrated the effectiveness of the proposed N-SVR technique through numerical experiments.
• Validated N-SVR on artificial datasets, UCI datasets, and short-term wind speed prediction.
• Achieved superior regression performance compared to standard methods under non-Gaussian noise conditions.

Conclusions:

• The proposed N-SVR technique effectively handles general noise models beyond Gaussian distributions.
• N-SVR offers a more robust and accurate regression solution for real-world applications with non-standard noise.
• This advancement has significant implications for fields like renewable energy forecasting and signal processing.