Computes the mean absolute error (MAE) for ensemble forecasting models.
A model fit to ensemble forecasting data.
The dates for which the CRPS and MAE will be computed.
These dates must be consistent with
Included for generic function compatibility.
This method is generic, and can be applied to all ensemble forecasting
Note the model may have been applied to a power transformation of the data, but that information is included in the input
the output is transformed appropriately.
A vector giving the MAE for the deterministic forecasts associated with the raw ensemble and for the ensemble forecasting model. This is the mean absolute difference of the raw ensemble medians and the observations, and the mean absolute difference of the median forecast and the observations (as in Sloughter et al. 2007). \ Note that Raftery et al. 2005 uses the mean absolute difference of the raw ensemble means and the observations, and the mean absolute difference of the BMA predictive mean and the observations.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
R Package for Probabilistic Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2007 (revised in 2010).
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data(ensBMAtest) ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo") obs <- paste("T2","obs", sep = ".") ens <- paste("T2", ensMemNames, sep = ".") tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], dates = ensBMAtest[,"vdate"], observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], forecastHour = 48, initializationTime = "00") ## Not run: # R check tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30) ## End(Not run) MAE( tempTestFit, tempTestData)