Ensemble empirical mode decomposition: Testing and objective automation

Abstract

Ensemble Empirical Mode Decomposition (EEMD), a recently developed improvement over traditional Empirical Mode Decomposition (EMD), utilises the concept of noise assisted data analysis to decompose a time series into Intrinsic Mode Functions (IMFs) and a residual (or trend). Because the EEMD algorithm is locally adaptive it is robust when applied to non-stationary and non-linear data and is suitable for decomposing hydroclimatic time series that appear non-stationary in terms of mean and variance. Using synthetic time series constructed with fluctuations of known frequency and a range of trends (linear, ramp, step and parabolic), we report results from our investigation into the ability of E..