R/NHSdata.R

In thief: Temporal Hierarchical Forecasting

#' Accident and Emergency demand in the UK
#'
#' Weekly demand of Accident & Emergency departments in the UK, 
#' from 7 November 2010 to 7 June 2015.
#'
#' @docType data
#'
#' @usage AEdemand
#'
#' @format An object of class \code{ts}.
#'
#' @keywords datasets
#'
#' @examples
#' library(ggplot2)
#' autoplot(AEdemand, xlab="Year", ylab="Demand ('000)") +
#'   ggtitle("Accident & Emergency Demand in the UK")
#' 
#' \dontrun{
#' 
#' # Demonstration of the adjustment of all temporal aggregates
#' # using Total Emergency Admissions
#' 
#' total <- AEdemand[,12]
#' totalagg <- tsaggregates(total)
#' plot(totalagg, main="Total Emergency Admissions")
#' 
#' # Base forecasts
#' base <- list()
#' for(i in 1:5)
#'   base[[i]] <- forecast(auto.arima(totalagg[[i]]))
#' base[[6]] <-  forecast(auto.arima(totalagg[[6]]), h=2)
#'
#' # Reconciled forecasts
#' reconciled <- reconcilethief(base)
#'
#' main <- paste(names(totalagg)," (k=",
#'            52/unlist(lapply(reconciled,frequency)),")",sep="")
#' par(mfrow=c(2,3))
#' for(i in 6:1)
#' {
#'   ylim <- range(base[[i]]$mean, base[[i]]$x, reconciled[[i]]$mean)
#'   plot(base[[i]], main=main[i], fcol='white',
#'       plot.conf=FALSE, ylim=ylim, xlim=c(2010.5,2017.5))
#'   polygon(c(2015.45, 2020, 2020, 2015.45),
#'           c(0, 0, 1e5, 1e5), col='grey', border=FALSE)
#'   lines(base[[i]]$mean, col='red', lty=2)
#'   lines(reconciled[[i]]$mean, col='blue')
#' }
#' }
"AEdemand"