A FRBE model of class frbe as returned by the frbe function.
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
frbe.
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 smape, mase, and rmse, for more details.
Details
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.
Value
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.
Author(s)
Michal Burda
References
<c3><85><c2><a0>t<c3><84><c2><9b>pni<c3><84><c2><8d>ka, M., Burda, M., <c3><85><c2><a0>t<c3><84><c2><9b>pni<c3><84><c2><8d>kov<c3><83><c2><a1>, L. Fuzzy Rule Base Ensemble Generated from Data
by Linguistic Associations Mining. FUZZY SET SYST. 2015.
See Also
frbe,
smape,
mase,
rmse
Examples
# 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)