Takes a time series as input and produces forecasts using the temporal hierarchical approach of Athanasopoulos et al (2016).
thief(y, m = frequency(y), h = m * 2, comb = c("struc", "mse", "ols", "bu", "shr", "sam"), usemodel = c("ets", "arima", "theta", "naive", "snaive"), forecastfunction = NULL, aggregatelist = NULL, ...)
y  Time series input 

m  Seasonal period 
h  Forecast horizon 
comb  Combination method of temporal hierarchies, taking one of the following values:

usemodel  Model used for forecasting each aggregation level:

forecastfunction  Userdefined function to be used instead of 
aggregatelist  Userselected list of forecast aggregates to consider 
...  Arguments to be passed to the time series modelling function
(such as 
forecast object.
This function computes the temporal aggregates of y
using
tsaggregates
, then calculates all forecasts using the model function
specified by usemodel
or forecastfunction
, and finally reconciles the
forecasts using reconcilethief
. The reconciled forecasts of y
are returned.
# NOT RUN { # Select ARIMA models for all series using auto.arima() z < thief(AEdemand[,12], usemodel='arima') plot(z) # Use your own function ftbats < function(y,h,...){forecast(tbats(y),h,...)} z < thief(AEdemand[,12], forecastfunction=ftbats) plot(z) # }