A machine learning enhanced empirical mode decomposition

Abstract

Empirical mode decomposition (EMD) is a fully data driven method for decomposing signals into a set of AM-FM components known as intrinsic mode functions (IMFs). Despite its usefulness in the analysis of real world signals, the process is rather deterministic and sensitive to parameters such as local envelope estimation. A combination of EMD and machine learning is proposed which provides an algorithm that is more robust to EMD parameters. In addition, the proposed extension is fully adaptive and facilitates the "data fusion via fission" mode of operation. The derivation and analysis of the proposed framework is supported with simulations in denoising and prediction applications.