Adjusted empirical mode decomposition with improved performance for signal modeling and prediction

An adjusted empirical mode decomposition method, built on Student´s probability density function is presented. Compared to the original EMD, the new version provides a lower number of intrinsic mode functions and is more accurate in signal modeling and prediction. Using a backpropagation neural network for learning and in-sample prediction, our experimental results on a synthetic signal, an electrocardiogram (ECG), and a financial time series show that the presented tEMD is more efficient and leads to higher prediction accuracy than conventional EMD, regardless of the input time signal.