Combination modelling

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())

Arguments

...	Model definitions used in the combination.
cmbn_fn	A function used to produce the combination.
cmbn_args	Additional arguments passed to cmbn_fn.

Details

A combination model can also be produced using mathematical operations.

Examples

if (requireNamespace("fable", quietly = TRUE)) {
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
  )

# An inverse variance weighted ensemble.
aus_production %>%
  model(
    cmbn1 = combination_model(
      SNAIVE(Beer), TSLM(Beer ~ trend() + season()),
      cmbn_args = list(weights = "inv_var")
    )
  )
}
#> Warning: 1 error encountered for cmbn1
#> [1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> Warning: 1 error encountered for cmbn2
#> [1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> Warning: 1 error encountered for cmbn1
#> [1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> # A mable: 1 x 1
#>          cmbn1
#>        <model>
#> 1 <NULL model>