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#' EMD decomposition
#'
#' Decompose input data to Intrinsic Mode Functions (IMFs) with the
#' Empirical Mode Decomposition algorithm.
#'
#' This is a wrapper around \code{eemd} with \code{ensemble_size = 1} and \code{noise_strength = 0}.
#'
#' @export
#' @name emd
#' @param input Vector of length N. The input signal to decompose.
#' @param 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.
#' @param 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 \code{S_number} is
#' zero, this stopping criterion is ignored. Default is 4.
#' @param num_siftings Use a maximum number of siftings as a stopping criterion. If
#' \code{num_siftings} is zero, this stopping criterion is ignored. Default is 50.
#' @return Time series object of class \code{"mts"} where series corresponds to
#' IMFs of the input signal, with the last series being the final residual.
#' @references
#' \enumerate{
#' \item{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}
#' }
#' @seealso \code{\link{eemd}}, \code{\link{ceemdan}}
emd <- function(input, num_imfs = 0, S_number = 4L, num_siftings = 50L) {
if (!all(is.finite(input)))
stop("'input' must contain finite values only.")
if (num_imfs < 0)
stop("Argument 'num_imfs' must be non-negative integer.")
if (S_number < 0)
stop("Argument 'S_number' must be non-negative integer.")
if (num_siftings < 0)
stop("Argument 'num_siftings' must be non-negative integer.")
output <- eemdR(input, num_imfs, ensemble_size = 1L,
noise_strength = 0L, S_number, num_siftings,
rng_seed = 0L, threads = 0L)
if (inherits(input, "ts")) {
tsp(output) <- tsp(input)
} else tsp(output) <- c(1, nrow(output), 1)
if (ncol(output) > 1) {
class(output) <- c("mts", "ts", "matrix")
colnames(output) <- c(paste("IMF", 1:(ncol(output) - 1)), "Residual")
} else class(output) <- "ts"
output
}
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