emd: EMD decomposition
In Rlibeemd: Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

emd	R Documentation

EMD decomposition

Decompose input data to Intrinsic Mode Functions (IMFs) with the Empirical Mode Decomposition algorithm.

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 S-number 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 `S_number` is zero, this stopping criterion is ignored. Default is 4.
`num_siftings`	Use a maximum number of siftings as a stopping criterion. If `num_siftings` is zero, this stopping criterion is ignored. Default is 50.

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