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

 evalfrbe R Documentation

## Evaluate the performance of the FRBE forecast

### Description

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

### Usage

```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()`

### 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)

```

lfl documentation built on Sept. 8, 2022, 5:08 p.m.