Weighted Prediction Averaging for Forecasts — mlr_pipeops

Performs (weighted) averaging of forecast PredictionFcsts, mirroring mlr3pipelines::PipeOpRegrAvg but preserving the forecast prediction type, which plain regravg would drop. The output is a PredictionFcst that keeps the time index and key columns (carried in the extra slot), so $order, $key, autoplot.PredictionFcst(), and forecast task_type inference keep working through the ensemble.

Connect it to several PipeOpLearner outputs (classical forecast learners or RecursiveForecaster / DirectForecaster) to average their forecasts.

Parameters

The parameters are the parameters inherited from mlr3pipelines::PipeOpRegrAvg.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpEnsemble -> mlr3pipelines::PipeOpRegrAvg -> PipeOpFcstAvg

Methods

Inherited methods

`PipeOpFcstAvg$new()`

Initializes a new instance of this Class.

Usage

PipeOpFcstAvg$new(
  innum = 0L,
  collect_multiplicity = FALSE,
  id = "fcstavg",
  param_vals = list()
)

Arguments

innum: (numeric(1))
Number of input channels. Default 0 creates a vararg channel taking an arbitrary number of inputs.
collect_multiplicity: (logical(1))
If TRUE, the single input is a Multiplicity collecting channel. Requires innum = 0. Default FALSE.
id: (character(1))
Identifier of resulting object, default "fcstavg".
param_vals: (named list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

`PipeOpFcstAvg$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpFcstAvg$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(mlr3pipelines)
task = tsk("airpassengers")
graph = gunion(list(
  po("learner", lrn("fcst.auto_arima"), id = "arima"),
  po("learner", lrn("fcst.ets"), id = "ets")
)) %>>%
  po("fcstavg")
flrn = as_learner(graph)$train(task)
forecast(flrn, task, 12L)
#> 
#> ── <PredictionFcst> for 12 observations: ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>       month row_ids truth response
#>  1961-01-01       1    NA 443.7184
#>  1961-02-01       2    NA 427.2568
#>  1961-03-01       3    NA 472.9142
#>         ---     ---   ---      ---
#>  1961-10-01      10    NA 478.5799
#>  1961-11-01      11    NA 413.0403
#>  1961-12-01      12    NA 458.7385