R/XFcstTrueCorr1.R

In yymmhaha/PackPaper1: Package related to my first paper

#' Forecast for covariate matrix: using true generating process (correlated series type 1)
#'
#' @param horizon a number. Forecast horizon.
#' @param sigmax a number. Standard deviation of noise of x.
#' @param p.block a list. Number of covariates x for each block. I think it's useless here.
#' @param A.block a list of 3 matrices. Each contains 3 AR coefficient matrices for the 3 lags.
#' @param Xinput a matrix. The original training X matrix for forecast, DAT$X.for.fcst (10 series)
#' @param nsize a number. Size (nrow(Xinput)).
#'
#' @return a matrix
#'
#' \item{X.trueforecast}{a matrix. Forecast values for each series produced by true method, simple scenario.}
#'
#'
#' @export
#'
#' @examples
#' ##


XTrueCorr <- function(horizon,
                      sigmax,
                      p.block,
                      A.block,
                      Xinput,
                      nsize){

  initialX <- matrix(rep(0, (horizon + 3)*10), (horizon+3), 10)  # 7+3 rows, 10 col
  initialX[1:3, ] <- Xinput[((nsize-2):nsize), ]


  for (m in 1:horizon){
    # ----------- no intervention

    x.last <- list(x1 = initialX[(m+2), ],
                   x2 = initialX[(m+1), ],
                   x3 = initialX[m, ])

    # need to separate the 3
    x1.last <- list(x1 = x.last$x1[1:3],
                    x2 = x.last$x2[1:3],
                    x3 = x.last$x3[1:3])

    tempX1 <- A.block$block1$A1 %*% x1.last$x1 + A.block$block1$A2 %*% x1.last$x2 + A.block$block1$A3 %*% x1.last$x3 + sigmax

    x2.last <- list(x1 = x.last$x1[4:6],
                    x2 = x.last$x2[4:6],
                    x3 = x.last$x3[4:6])

    tempX2 <- A.block$block2$A1 %*% x2.last$x1 + A.block$block2$A2 %*% x2.last$x2 + A.block$block2$A3 %*% x2.last$x3 + sigmax

    x3.last <- list(x1 = x.last$x1[7:10],
                    x2 = x.last$x2[7:10],
                    x3 = x.last$x3[7:10])

    tempX3 <- A.block$block3$A1 %*% x3.last$x1 + A.block$block3$A2 %*% x3.last$x2 + A.block$block3$A3 %*% x3.last$x3 + sigmax

    # combine the 3
    tempX <- c(tempX1, tempX2, tempX3)
    initialX[(m+3), ] <- tempX


  }
  X.trueforecast <- initialX[(4:10), ]
  return(X.trueforecast = X.trueforecast)

}