Fine tuning support vector machines for short-term wind speed forecasting

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The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling (2020, background): “Finally, the performance of the proposed models is compared and evaluated with eight models including PSF, ARIMA, least squares support vector machine (LSSVM) methods and their hybridized models with EMD and EEMD methods.”
A review of combined approaches for prediction of short-term wind speed and power (2014, methodology): “proposed Least Squares SVM (LSSVM), which is a reformulation of the SVM problem in an easier form, for one-step-ahead wind speed forecasting and obtained reasonable accuracy levels compared to the persistence approach thanks to the fine tuning of the parameters [21].”
Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, methodology): “Tripathi et al. (2006), Anandhi et al. (2009) and Zhou et al. (2011) applied LS-SVM-R successfully for downscaling GCM outputs to catchmentscale climatic variables.”
Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, methodology): “It could be modeled by various methods, including the persistence method, autoregressive models [13], [14], artificial neural networks (ANNs) [15], SVR machines [16]–[18], and the hybrid method [19].”