Entropic Empirical Mode Decomposition
Methodology
2016-01-27 v3
Abstract
Empirical Mode Decomposition(EMD) is an adaptive data analysis technique for analyzing nonlinear and nonstationary data[1]. EMD decomposes the original data into a number of Intrinsic Mode Functions(IMFs)[1] for giving better physical insight of the data. Permutation Entropy(PE) is a complexity measure[3] function which is widely used in the field of complexity theory for analyzing the local complexity of time series. In this paper we are combining the concepts of PE and EMD to resolve the mode mixing problem observed in determination of IMFs.
Cite
@article{arxiv.1507.03157,
title = {Entropic Empirical Mode Decomposition},
author = {Sumit Kumar Ram and Marta Molinas},
journal= {arXiv preprint arXiv:1507.03157},
year = {2016}
}