| data.frc | R Documentation | 
Wrapper to forecasts data.frames with a single call.
data.frc(data.in,method=c("crost","crost.ma","tsb","sexsm","imapa","auto"),...)
data.in | 
 Data frame with time series. This can also be a matrix or array with each column being a different time series.  | 
method | 
 Which method to use for forecasting: "crost", "crost.ma", "tsb", "sexsm", "imapa", "auto". "auto" uses PKa classification to select automatically between Croston, SBA and SES.  | 
... | 
 Additional inputs to pass to forecasting functions. See individual function documentation for options.  | 
frc.out | 
 Data frame containing forecasts for all time series.  | 
out | 
 List with detailed output per series. To access individual outputs of the list use: sapply(out, get, x="element"), where "element" could be for example "frc.in".  | 
Nikolaos Kourentzes
By default methods are optimised using the cost functions introduced by: N. Kourentzes, 2014, On intermittent demand model optimisation and selection, International Journal of Production Economics, 156: 180-190. doi: 10.1016/j.ijpe.2014.06.007.
The PK approximate classification is described in: F. Petropoulos and N. Kourentzes, 2015, Journal of Operational Research Society. https://link.springer.com/article/10.1057/jors.2014.62. https://kourentzes.com/forecasting/2014/05/13/forecast-combinations-for-intermittent-demand/
crost, crost.ma, tsb, sexsm, imapa, idclass.
data.frc(simID(10,30),method="crost",type="sba",h=5)$frc.out
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