The forecastHybrid package can produce some robust cross-validation weighted ensembles. Unfortunately, it handles confidence intervals by taking an unweighted mean of all candidate models or using the max/min for the candidate models.
As we only use samples from models (except when samples = 1 and a point forecast is used) this behaviour is a problem. In the current wrapper forecastHybrid_model we take samples by assuming the forecast is normally distributed and reconstructing a mean and standard deviation from the upper and lower bounds. This means that we see no benefit of any kind from a weighted ensemble. In an ideal scenario, the underlying package would produce better samples for its forecasts but failing that a more elegant workaround would be ideal.
The
forecastHybridpackage can produce some robust cross-validation weighted ensembles. Unfortunately, it handles confidence intervals by taking an unweighted mean of all candidate models or using the max/min for the candidate models.As we only use samples from models (except when
samples = 1and a point forecast is used) this behaviour is a problem. In the current wrapperforecastHybrid_modelwe take samples by assuming the forecast is normally distributed and reconstructing a mean and standard deviation from the upper and lower bounds. This means that we see no benefit of any kind from a weighted ensemble. In an ideal scenario, the underlying package would produce better samples for its forecasts but failing that a more elegant workaround would be ideal.