Combines multiple model definitions (passed via ...
) to produce a model
combination definition using some combination function (cmbn_fn
). Currently
distributional forecasts are only supported for models producing normally
distributed forecasts.
combination_model(..., cmbn_fn = combination_ensemble, cmbn_args = list())
... | Model definitions used in the combination. |
---|---|
cmbn_fn | A function used to produce the combination. |
cmbn_args | Additional arguments passed to |
A combination model can also be produced using mathematical operations.
library(fable) library(tsibble) library(tsibbledata) # cmbn1 and cmbn2 are equivalent and equally weighted. aus_production %>% model( cmbn1 = combination_model(SNAIVE(Beer), TSLM(Beer ~ trend() + season())), cmbn2 = (SNAIVE(Beer) + TSLM(Beer ~ trend() + season()))/2 ) #> # A mable: 1 x 2 #> cmbn1 cmbn2 #> <model> <model> #> 1 <COMBINATION> <COMBINATION> # An inverse variance weighted ensemble. aus_production %>% model( cmbn1 = combination_model( SNAIVE(Beer), TSLM(Beer ~ trend() + season()), cmbn_args = list(weights = "inv_var") ) ) #> # A mable: 1 x 1 #> cmbn1 #> <model> #> 1 <COMBINATION>