Comparison of Gaussian and ANOVA Kernel in Support Vector Regression for Predicting Coal Price

Coal price prediction is needed as one of the supports for coal industry to make transaction. Prediction result can be used to make next budgeting for the buyer or manage the profit for the seller. We propose Support Vector Regression (SVR) method to predict coal price. Before calculating regression function there is mapping data stage, hessian matrix. Kernel for hessian matrix stage can determine accuracy of prediction. Therefore, Gaussian RBF kernel and ANOVA kernel are used and analyzed the effects. To obtain predictive results with good accuracy, testing of each parameter is performed and evaluated by mean absolute percentage error (MAPE). The averages MAPE for testing are 9,64% with Gaussian kernel and 8,38% with ANOVA kernel, which is categorized very well. The predicted results of both kernels are not too different, but the ANOVA kernel works better on this coal price data.