Nothing
#' Function to predict with EMD-PSF,ARIMA model
#'
#' @param data as input time series data
#' @param n.ahead as horizon of values to be predicted
#' @return predicted values with EMD-PSF,ARIMA model
#' @import Rlibeemd
#' @import forecast
#' @import tseries
#' @export
#' @examples
#' # emdpsfarima(data = nottem, n.ahead = 6)
emdpsfarima <- function(data, n.ahead)
{
options(warn=-1)
a <- emd(data)
#b <- eemd(data)
a_imf <- ncol(a)
#b_imf <- ncol(b)
#=====================================================
#EMD-PSF,ARIMA
#=====================================================
xn <- NULL
yn <- 0
#a <- emd(data)
for(i in 1:a_imf)
{
if (kpss.test(a[,i], null = "Trend")$p.value == 0.1)
{
xn[[i]] <- lpsf(data = a[,i], n.ahead = n.ahead)
}
else
{
dummy <- forecast(auto.arima(a[,i]), n.ahead)$mean
xn[[i]] <- as.numeric(dummy)
}
yn <- yn + xn[[i]]
}
emd_psf_ar <- yn
#=====================================================
return(emd_psf_ar)
}
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