Clock Differences Prediction Algorithm Based on EMD-SVM

A new prediction algorithm based on Empirical model decomposition (EMD) and Support vector machine (SVM) is put forward in this paper, and this algorithm solves the problem of the hydrogen atomic clock differences prediction, which is affected by the non-linearity and non-stability. The clock differences were decomposed into Intrinsic mode functions (IMF) and the residual series. The suitable kernel function and parameters were chosen to build the different SVM for predicting each IMF and the residual series. Each prediction result was summed to obtain the clock differences prediction. Results show that the EMD-SVM algorithm is effective compared with the linear regression and single SVM. The relative prediction error is reduced from 0.4327% to 0.2371%, and the dispersion is less than other methods.