Description Usage Arguments Details Value Author(s) References See Also Examples
Take a FRBE forecast and compare it with real values using arbitrary error function.
1 2 3 
fit 
A FRBE model of class 
real 
A numeric vector of real (known) values. The vector must correspond to the values
being forecasted, i.e. the length must be the same as the horizon forecasted by

error 
Error measure to be computed. It can be either Symmetric Mean Absolute Percentage
Error (SMAPE), Mean Absolute Scaled Error (MASE), or Root Mean Squared Error (RMSE).
See 
Take a FRBE forecast and compare it with real values by evaluating a given error measure. FRBE forecast should be made for a horizon of the same value as length of the vector of real values.
Function returns a data.frame with single row and columns corresponding to the error of the individual forecasting methods that the FRBE is computed from. Additionally to this, a column "avg" is added with error of simple average of the individual forecasting methods and a column "frbe" with error of the FRBE forecasts.
Michal Burda
Štěpnička, M., Burda, M., Štěpničková, L. Fuzzy Rule Base Ensemble Generated from Data by Linguistic Associations Mining. FUZZY SET SYST. 2015.
1 2 3 4 5 6 7 8 9 10  ## Not run:
# prepare data (from the forecast package)
library(forecast)
horizon < 10
train < wineind[1 * (length(wineind)horizon+1):length(wineind)]
test < wineind[(length(wineind)horizon+1):length(wineind)]
f < frbe(ts(train, frequency=frequency(wineind)), h=horizon)
evalfrbe(f, test)
## End(Not run)

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