Prediction using Gaussian Process Regression and Support Vector Regression

الملخص

In this research study Gaussian Process Regression (GPR) and Support Vector Regression (SVR) are considered one of the most important techniques of automated learning, they are used to analyze various data sets and generate predictions with high prediction accuracy.

In this study, we proposed new method to improve prediction by integrating predictions Support vector regression method and Gaussian Process Regression method and their quality was verified by applying them on both artificial and realistic data. This method was also compared with the Support vector regression method and Gaussian Process Regression using the measurements of prediction error explanation (MSE, RMSE, MAPE), in order to obtain the ideal method to improve prediction accuracy.

The proposed method gives the best predictive accuracy and better results in order to replicate the number of preference based on the smallest value of the values of the error measures used , because of the ability of the regression curve ideals have an appropriate and better data representation.