Abstract :
Support Vector Machines (SVMs) have become one of the most popular methods in Machine Learning during the last few years, but its performance mainly depends on the selection of optimal parameters which is very complex. In this study, the SCEM-UA algorithm developed by Vrugt is employed for parameters selection of Support Vector Regression (SVR). The SCEM-UA algorithm, which operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling, can avoid the tendency of falling into local minima. The proposed method was tested on a complicated nonlinearly runoff forecasting. The results illustrated that SCEM-UA algorithm can successfully identify the optimal parameters of SVR than grid search method, and can achieve an accurate prediction. Keywords: Support Vector Machines; Optimization; SCEM-UA; Time series; Forecasting
