# evalfrbe: Evaluate the performance of the FRBE forecast In beerda/lfl: Linguistic Fuzzy Logic

## Description

Take a FRBE forecast and compare it with real values using arbitrary error function.

## Usage

 `1` ```evalfrbe(fit, real, error = c("smape", "mase", "rmse")) ```

## Arguments

 `fit` 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 also `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.

Michal Burda

## References

Štěpnička, M., Burda, M., Štěpničková, L. Fuzzy Rule Base Ensemble Generated from Data by Linguistic Associations Mining. FUZZY SET SYST. 2015.

`frbe()`, `smape()`, `mase()`, `rmse()`
 ```1 2 3 4 5 6 7``` ``` # 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) ```