rollingForecasts5 = function (y, forecastfunction, h = 12, level = c(80, 95), fcast_args=NULL, fitfunction=NULL, fit_args = NULL, doPar=T)
{
forecastfunction = match.fun(forecastfunction)
#y <- as.ts(y)
if(is.ts(y)) {
n <- length(y)
} else {
n = nrow(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("df_data"))
parallel::clusterEvalQ(cl, expr = {library(prophet); library(zoo)})
#parallel::clusterExport(cl, varlist = c("y", "n", "h", "level", "fcast_args", "forecastfunction", "fit_args", "fitfunction"), 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")
)
)
if(is.ts(y)) {
results = parallel::parLapply(cl,
X = seq_len(n),
fun = function(i){
if(is.null(fitfunction)) {
try(do.call(forecastfunction, append(list(y = subset(y, end = i),h = h,level = level), fcast_args)),
silent = TRUE)
} else {
fit = try(do.call(fitfunction, append(list(y=subset(y, end = i)), fit_args)),
silent = TRUE)
try(do.call(forecastfunction, append(list(object = fit, h = h, level = level),
fcast_args)),
silent = TRUE)
}
})
} else {
# results = parallel::parLapply(cl,
# X = seq_len(n),
# fun = function(i){
# if(is.null(fitfunction)) {
# try(do.call(forecastfunction, append(list(y = subset(y,ds <= y$ds[i]),h = h), fcast_args)),
# silent = TRUE)
# } else {
# fit = try(do.call(fitfunction, append(list(y = subset(y,ds <= y$ds[i])), fit_args)),
# silent = TRUE)
# try(do.call(forecastfunction, append(list(object = fit, h = h),
# fcast_args)),
# silent = TRUE)
# }
# })
results = lapply(37:n,
FUN = function(i){ m = try(prophet::prophet(df = subset(df_data, ds <= df_data$ds[i]), yearly.seasonality = T));
future = try(prophet::make_future_dataframe(m, 13, freq = "m"));
future = try(as.Date.numeric(ifelse(lubridate::day(future$ds) < 10, future$ds - lubridate::day(future$ds), future$ds)));
return(try(predict(m, future)))
})
}
results = vector(mode = "list", length = nrow(df_data)-37+1)
for(i in 37:nrow(df_data)) {
m = try(prophet::prophet(df = subset(df_data, ds <= df_data$ds[i]), yearly.seasonality = T));
future = try(prophet::make_future_dataframe(m = m, periods = 13, freq = "m", include_history = F));
future$ds = try(zoo::as.Date.numeric(ifelse(lubridate::day(future$ds) < 10, future$ds - lubridate::day(future$ds), future$ds)));
results[[i-37+1]]=try(predict(object = m, df = future))
saveRDS(object = results, file = "prophet_results.rds")
}
parallel::stopCluster(cl)
for (i in 37:nrow(df_data)) {
if (!is.element("try-error", class(results[[i-37+1]]))) {
fcasts[i, 1:h, 1] <- results[[i-37+1]]$yhat[1:h]
if("lower" %in% names(results[[i-37+1]])) {
for(j in 1:length(level)) {
fcasts[i, 1:h, 1 + j] <- results[[i-37+1]][1:h,"yhat_lower"]
fcasts[i, 1:h, 1 + length(level) + j] <- results[[i-37+1]][1:h,"yhat_upper"]
}
}
fcasts[i, 1:h, 1 + 2*length(level) + 1] <- y[(i+1):(i+h), "y"]
fcasts[i, 1:h, 1 + 2*length(level) + 2] <- fcasts[i, 1:h, 1] - y[(i+1):(i+h), "y"]
}
}
return(result)
} else if(doPar==F){
results = lapply(1:seq_len(n), FUN = try(forecastfunction(subset(y,end = i), h = h, level = level), silent = TRUE))
# 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(results)
}
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