rollingForecasts2 = function (y, forecastfunction, h = 12, level = c(80, 95), fcast_args=NULL, doPar=T)
{
forecastfunction = match.fun(forecastfunction)
y <- as.ts(y)
n <- length(y)
fcasts = array(NA, dim = c(n, h, 2*length(level) + 3),
dimnames = list(forecast_date = 1:n,
steps_ahead = 1:h,
forecast = c("mean", paste0("lower_", level), paste0("upper_", level), "actual", "error")
)
)
if(doPar==T) {
#print(y)
nCore = parallel::detectCores()
cl = parallel::makeCluster(nCore)
parallel::clusterExport(cl, varlist = c("y", "n", "h", "level", "fcast_args", "forecastfunction"), envir = environment())
parallel::clusterEvalQ(cl, expr = {library(forecast) })
# fcasts = array(NA, dim = c(n, h, 2*length(level) + 3),
# dimnames = list(forecast_date = 1:n,
# steps_ahead = 1:h,
# forecast = c("mean", paste0("lower_", level), paste0("upper_", level), "actual", "error")
# )
# )
result = parallel::parLapply(cl,
X = seq_len(n),
fun = function(i) try(do.call(forecastfunction, append(list(y = subset(y,end = i),h = h,level = level), fcast_args)),
silent = TRUE))
parallel::stopCluster(cl)
for (i in seq_len(n)) {
if (!is.element("try-error", class(result[[i]]))) {
fcasts[i, 1:h, 1] <- result[[i]]$mean[1:h]
for(j in 1:length(level)) {
fcasts[i, 1:h, 1 + j] <- result[[i]]$lower[1:h, j]
fcasts[i, 1:h, 1 + length(level) + j] <- result[[i]]$upper[1:h, j]
}
fcasts[i, 1:h, 1 + 2*length(level) + 1] <- y[(i+1):(i+h)]
fcasts[i, 1:h, 1 + 2*length(level) + 2] <- fcasts[i, 1:h, 1] - y[(i+1):(i+h)]
}
}
#return(result)
} else if(doPar==F){
for (i in seq_len(n)) {
result <- try(forecastfunction(subset(y,end = i), h = h, level = level), silent = TRUE)
if (!is.element("try-error", class(result))) {
fcasts[i, 1:h, 1] <- result$mean[1:h]
for(j in 1:length(level)) {
fcasts[i, 1:h, 1 + j] <- result$lower[1:h, j]
fcasts[i, 1:h, 1 + length(level) + j] <- result$upper[1:h, j]
}
fcasts[i, 1:h, 1 + 2*length(level) + 1] <- y[(i+1):(i+h)]
fcasts[i, 1:h, 1 + 2*length(level) + 2] <- fcasts[i, 1:h, 1] - y[(i+1):(i+h)]
}
}
}
return(fcasts)
}
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