R/binarization.R

In otsfeatures: Ordinal Time Series Analysis

#' Constructs the binarized time series associated with a given
#' ordinal time series
#'
#' \code{binarization} constructs the binarized time series associated with a given
#' ordinal time series.
#'
#' @param series An OTS (numerical vector with integers).
#' @param states A numeric vector containing the corresponding
#' states.
#' @return The binarized time series.
#' @examples
#' binarized_series <- binarization(AustrianWages$data[[100]],
#' states = 0 : 5) # Constructing the binarized
#' # time series for one OTS in dataset AustrianWages
#' @details
#' Given an OTS of length \eqn{T} with range \eqn{\mathcal{S}=\{s_0, s_1, s_2, \ldots, s_n\}} (\eqn{s_0 < s_1 < s_2 < \ldots < s_n}),
#' \eqn{\overline{X}_t=\{\overline{X}_1,\ldots, \overline{X}_T\}}, the function
#' constructs the binarized time series, which is defined as
#' \eqn{\overline{\boldsymbol Y}_t=\{\overline{\boldsymbol Y}_1, \ldots, \overline{\boldsymbol Y}_T\}},
#' with \eqn{\overline{\boldsymbol Y}_k=(\overline{Y}_{k,0}, \overline{Y}_{k,1},\ldots, \overline{Y}_{k,n})^\top}
#' such that \eqn{\overline{Y}_{k,i}=1} if \eqn{\overline{X}_k=s_i} (\eqn{k=1,\ldots,T,
#' , i=0,\ldots,n}). The binarized series is constructed in the form of a matrix
#' whose rows represent time observations and whose columns represent the
#' states in the original series.
#' @encoding UTF-8
#' @author
#' Ángel López-Oriona, José A. Vilar
#' @references{
#'
#'   \insertRef{weiss2018introduction}{otsfeatures}
#'   \insertRef{lopez2023hard}{otsfeatures}
#'
#' }
#' @export

binarization <- function(series, states) {

  check_ots(series)
  series_length <- length(series)
  n_states <- length(states)
  matrix_binarization <- base::matrix(0, nrow = series_length, ncol = n_states)

  for (i in 1 : n_states) {

    matrix_binarization[,i][which(series == states[i])] <- 1

  }

  return(matrix_binarization)

}