Description Usage Arguments Details Value See Also
Decompose input data to Intrinsic Mode Functions (IMFs) with the Empirical Mode Decomposition algorithm.
1  emd(input, num_imfs = 0, S_number = 4L, num_siftings = 50L)

input 
Vector of length N. The input signal to decompose. 
num_imfs 
Number of Intrinsic Mode Functions (IMFs) to compute. If num_imfs is set to zero, a value of num_imfs = emd_num_imfs(N) will be used, which corresponds to a maximal number of IMFs. Note that the final residual is also counted as an IMF in this respect, so you most likely want at least num_imfs=2. 
S_number 
Integer. Use the Snumber stopping criterion [1] for the EMD procedure with the given values of S.
That is, iterate until the number of extrema and zero crossings in the
signal differ at most by one, and stay the same for S consecutive
iterations. Typical values are in the range 3–8. If 
num_siftings 
Use a maximum number of siftings as a stopping criterion. If

This is a wrapper around eemd
with ensemble_size = 1
and noise_strength = 0
.
Time series object of class "mts"
where series corresponds to
IMFs of the input signal, with the last series being the final residual.
@references
N. E. Huang, Z. Shen and S. R. Long, "A new view of nonlinear water waves: The Hilbert spectrum", Annual Review of Fluid Mechanics, Vol. 31 (1999) 417–457
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