An Extrema Extension Method Based on Support Vector Regression for Restraining the End Effects in Empirical Mode Decomposition

The end effects is a serious problem in the applications of the empirical mode decomposition (EMD) method. To deal with this problem, an extrema extension method based on the support vector regression (SVR) is proposed in this paper. In each iterating process of the EMD method, the SVR method is employed to predict one maximum and a minimum point respectively at the both ends of the original data series to form the relatively true upper and lower envelope, thus the end effects can be restrained effectively. The prediction of an extrema point includes two parts, the forecast of the extreme value and location. In contrast with other traditional extrema extension methods, such as the extrema mirror extension and linear fitting extension method, the decomposed results from the simulation and actual signals demonstrated that this proposed method has a better performance in eliminating the end effects related to the empirical mode decomposition.