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Create a Direct Forecast Learner

Source: R/direct_forecaster.R
direct_forecaster.Rd

Function to create a DirectForecaster object. This is the recommended way to construct a direct forecaster; it is a thin wrapper around DirectForecaster$new().

A direct forecaster trains a separate regression model per forecast horizon, so predictions never feed back into one another (no error accumulation). For the recursive strategy (a single iterated model) see recursive_forecaster().

Usage

direct_forecaster(
  learner,
  lags,
  horizons,
  id = NULL,
  param_vals = list(),
  predict_type = NULL
)

Arguments

learner

(mlr3::Learner | mlr3pipelines::Graph | mlr3pipelines::PipeOp)
A regression learner or a graph/PipeOp (without PipeOpFcstLags).

lags

(integer())
The base lag values. Exposed in $param_set as lags, so it can be tuned via mlr3tuning::AutoTuner.

horizons

(integer())
Either a single integer H (expanded to 1:H) or an integer vector of specific horizons. One model is trained per horizon.

id

(character(1) | NULL)
Identifier, default NULL (auto-generated from the learner id).

param_vals

(named list())
Hyperparameter values applied to every horizon model. Per-horizon hyperparameters are not currently supported.

predict_type

(character(1) | NULL)
The predict type, default NULL.

Value

DirectForecaster.

Examples

library(mlr3pipelines)

task = tsk("airpassengers")
split = partition(task, ratio = 0.8)

# one model per horizon
flrn = direct_forecaster(lrn("regr.rpart"), lags = 1:3, horizons = length(split$test))
flrn$train(task, split$train)
flrn$predict(task, split$test)
#> 
#> ── <PredictionRegr> for 29 observations: ───────────────────────────────────────
#>  row_ids truth response      month
#>      116   505 391.9375 1958-08-01
#>      117   404 324.7500 1958-09-01
#>      118   359 306.0000 1958-10-01
#>      ---   ---      ---        ---
#>      142   461 368.6875 1960-10-01
#>      143   390 364.9333 1960-11-01
#>      144   432 362.3571 1960-12-01