reconcilethief: Reconcile temporal hierarchical forecasts
In thief: Temporal Hierarchical Forecasting

Description Usage Arguments Value Author(s) See Also Examples

View source: R/combine.R

Takes forecasts of time series at all levels of temporal aggregation and combines them using the temporal hierarchical approach of Athanasopoulos et al (2016).

1 2	reconcilethief(forecasts, comb = c("struc", "mse", "ols", "bu", "shr", "sam"), mse = NULL, residuals = NULL, returnall = TRUE, aggregatelist = NULL)

`forecasts`	List of forecasts. Each element must be a time series of forecasts, or a forecast object. The number of forecasts should be equal to k times the seasonal period for each series, where k is the same across all series.
`comb`	Combination method of temporal hierarchies, taking one of the following values: "struc" Structural scaling - weights from temporal hierarchy "mse" Variance scaling - weights from in-sample MSE "ols" Unscaled OLS combination weights "bu" Bottom-up combination – i.e., all aggregate forecasts are ignored. "shr" GLS using a shrinkage (to block diagonal) estimate of residuals "sam" GLS using sample covariance matrix of residuals
`mse`	A vector of one-step MSE values corresponding to each of the forecast series.
`residuals`	List of residuals corresponding to each of the forecast models. Each element must be a time series of residuals. If `forecast` contains a list of forecast objects, then the residuals will be extracted automatically and this argument is not needed. However, it will be used if not `NULL`.
`returnall`	If `TRUE`, a list of time series corresponding to the first argument is returned, but now reconciled. Otherwise, only the most disaggregated series is returned.
`aggregatelist`	(optional) User-selected list of forecast aggregates to consider

List of reconciled forecasts in the same format as forecast. If returnall==FALSE, only the most disaggregated series is returned.

Rob J Hyndman

thief, tsaggregates

# Construct aggregates
aggts <- tsaggregates(USAccDeaths)

# Compute forecasts
fc <- list()
for(i in seq_along(aggts))
  fc[[i]] <- forecast(auto.arima(aggts[[i]]), h=2*frequency(aggts[[i]]))

# Reconcile forecasts
reconciled <- reconcilethief(fc)

# Plot forecasts before and after reconcilation
par(mfrow=c(2,3))
for(i in seq_along(fc))
{
  plot(reconciled[[i]], main=names(aggts)[i])
  lines(fc[[i]]$mean, col='red')
}