Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes the fuzzy rulebased ensemble of timeseries forecasts. Several forecasting methods are used to predict future values of given timeseries and a weighted sum is computed from them with weights being determined from a fuzzy rule base.
1  frbe(d, h = 10)

d 
A source timeseries in the ts timeseries format. Note that the frequency of the timeseries must to be set properly. 
h 
A forecasting horizon, i.e. the number of values to forecast. 
This function computes the fuzzy rulebased ensemble of timeseries forecasts. The evaluation comprises of the following steps:
Several features are extracted from the given timeseries d
:
length of the timeseries;
strength of trend;
strength of seasonality;
skewness;
kurtosis;
variation coefficient;
stationarity;
frequency. These features are used later to infer weights of the forecasting methods.
Several forecasting methods are applied on the given timeseries d
to
obtain forecasts. Actually, the following methods are used:
ARIMA  by calling forecast::auto.arima()
;
Exponential Smoothing  by calling forecast::ets()
;
Random Walk with Drift  by calling forecast::rwf()
;
Theta  by calling [forecast::thetaf().
Computed features are input to the fuzzy rulebased inference mechanism
which yields into weights of the forecasting methods. The fuzzy rule base is
hardwired in this package and it was obtained by performing data mining with
the use of the farules()
function.
A weighted sum of forecasts is computed and returned as a result.
Result is a list of class frbe
with the following elements:
features
 a data frame with computed features of the given timeseries;
forecasts
 a data frame with forecasts to be ensembled;
weights
 weights of the forecasting methods as inferred from the features
and the hardwired fuzzy rule base;
mean
 the resulting ensembled forecast (computed as a weighted sum
of 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 11 12 13 14  # 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)]
# perform FRBE
f < frbe(ts(train, frequency=frequency(wineind)), h=horizon)
# evaluate FRBE forecasts
evalfrbe(f, test)
# display forecast results
f$mean

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