Some code based on Rob's workaround of setting the negatives to zero and reconcile
# Re-reconciliate when zero values present
# Extract groups
groups <- hts.obj %>%
aggts() %>%
get_groups()
x=0 #Counter
# Loop until all positive
while(sum(hts.obj[[1]] <0) > 0){
# Generate all time series
hts.obj <- aggts(hts.obj)
# Overwrite negatives by zero
hts.obj[hts.obj<0] <- 0
# Reconcile
hts.obj <- hts.obj %>%
ts() %>%
combinef(groups = groups, keep ="gts")
# Count up
x=x+1
# Break after 10 loops
if(x>=10)break
}
rm("x")
# Overwrite remaining negatives by zero
hts.obj[[1]][hts.obj[[1]]<0] <- 0
In this example forecasts are indexed by [[1]] - this might change. Also note that overwriting zeros leads to biased forecasts.