thief (version 0.3)

# reconcilethief: Reconcile temporal hierarchical forecasts

## Description

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

## Usage

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

## Arguments

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

## Value

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

`thief`, `tsaggregates`

## Examples

```# NOT RUN {
# 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')
}

# }
```