CEEMD

From hht v2.1.3 by Daniel C Bowman

Complete Ensemble Empirical Mode Decomposition

This function implements the complete ensemble empirical mode decomposition (CEEMD) algorithm.

Keywords: ts

Usage

CEEMD(sig, tt, noise.amp, trials, verbose = TRUE,  spectral.method = "arctan", diff.lag = 1, tol = 5, max.sift = 200, stop.rule = "type5", boundary = "wave", sm = "none", smlevels = c(1), spar = NULL, max.imf = 100, interm = NULL,  noise.type = "gaussian", noise.array = NULL)

Arguments

sig: a time series to be decomposed (vector)
tt: The sample times of sig
noise.amp: Amplitude of white noise to use in denoising algorithm
trials: Number of times to run EMD
verbose: If TRUE, notify when each trial is complete
spectral.method: See Sig2IMF.
diff.lag: See Sig2IMF.
tol: See Sig2IMF.
max.sift: See Sig2IMF.
stop.rule: See Sig2IMF.
boundary: See Sig2IMF.
sm: See Sig2IMF.
smlevels: See Sig2IMF.
spar: See Sig2IMF.
max.imf: See Sig2IMF.
interm: See Sig2IMF.
noise.type: If unspecified or gaussian, produce a Gaussian noise series with length length(sig) and standard deviation noise.amp. If uniform, produce a uniform random distribution with length length(sig) and maximum absolute value of noise.amp. If custom, then use a custom noise array as defined in input parameter noise.array (see below).
noise.array: If noise.type = "custom", this array must be a TRIALS x LENGTH(TT) collection of time series to be used in the place of uniform or gaussian noise. Each row in the array corresponds to the noise series added for that particular trial during the CEEMD run. By default, noise.array = NULL.

Details

This function performs the complete ensemble empirical mode decomposition, a noise assisted empirical mode decomposition algorithm. The CEEMD works by adding a certain amplitude of white noise to a time series, decomposing it via EMD, and saving the result. In contrast to the Ensemble Empirical Mode Decomposition (EEMD) method, the CEEMD also ensures that the IMF set is quasi-complete and orthogonal. The CEEMD can ameliorate mode mixing and intermittency problems. Keep in mind that the CEEMD is a computationally expensive algorithm and may take significant time to run.

Value

ceemd.result: The final result of the CEEMD algorithm

References

Torres, M. E., Colominas, M. A., Schlotthauer, G., Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.4144-4147, doi: 10.1109/ICASSP.2011.5947265.

Examples


## Not run: 
# 
# data(PortFosterEvent)
# noise.amp <- 6.4e-07
# trials <- 100
# 
# ceemd.result <- CEEMD(sig, tt, noise.amp, trials)
# PlotIMFs(ceemd.result, imf.list = 1:6, time.span = c(5, 10))
# ## End(Not run)

Documentation reproduced from package hht, version 2.1.3, License: GPL (>= 3)

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