Rlibeemd: Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) doi:10.1007/s00180-015-0603-9, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version from GitHub: https://github.com/helske/Rlibeemd/.

Introducing libeemd: A program package for performing the ensemble empirical mode decomposition

Abstract The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend.