Box-Cox Transform the Target Variable — mlr_pipeops

Applies a Box-Cox transformation to the target variable to stabilize the variance, producing the new target BoxCox(y, lambda). The transformation is pointwise and monotonic, so no rows are dropped and predictions are inverted via forecast::InvBoxCox(). lambda = 0 is the log transformation. When lambda is NULL (default) it is estimated from the training data, per series on keyed tasks. Predicting a series not seen during training is an error.

Box-Cox and log transformations require strictly positive target values. Non-positive values produce NaN or an error. A negative estimated lambda can make forecast::InvBoxCox() return NA for upper quantiles. Set lower = 0 to avoid this.

Parameters

The parameters are the parameters inherited from mlr3pipelines::PipeOpTargetTrafo, as well as the following:

lambda :: numeric(1) | NULL
Box-Cox transformation parameter. NULL (default) estimates it from the training data, 0 is the log transformation, any other numeric is used as a fixed value.
method :: character(1)
Method used to estimate lambda when lambda = NULL, one of "guerrero" (default) or "loglik". See forecast::BoxCox.lambda().
lower :: numeric(1)
Lower bound for the estimated lambda. Default -1.
upper :: numeric(1)
Upper bound for the estimated lambda. Default 2.

Limitations

This PipeOp must not be placed inside a RecursiveForecaster or DirectForecaster graph and is rejected at construction. Use it inside a plain mlr3pipelines::GraphLearner via ppl("targettrafo", ...), or wrap the forecaster itself with ppl("targettrafo", ...) so all horizons are inverted together.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTargetTrafo -> PipeOpTargetTrafoBoxCox

Methods

Inherited methods

`PipeOpTargetTrafoBoxCox$new()`

Initializes a new instance of this Class.

Usage

PipeOpTargetTrafoBoxCox$new(id = "fcst.targetboxcox", param_vals = list())

Arguments

id: (character(1))
Identifier of resulting object, default "fcst.targetboxcox".
param_vals: (named list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

`PipeOpTargetTrafoBoxCox$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpTargetTrafoBoxCox$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# \donttest{
library(mlr3pipelines)
task = tsk("airpassengers")
split = partition(task, ratio = 0.8)
flrn = as_learner(ppl("targettrafo",
  graph = DirectForecaster$new(lrn("regr.rpart"), lags = 1:3, horizons = length(split$test)),
  trafo_pipeop = po("fcst.targetboxcox")
))
flrn$train(task, split$train)
flrn$predict(task, split$test)
#> 
#> ── <PredictionFcst> for 29 observations: ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>       month row_ids truth response
#>  1958-08-01     116   505 409.4023
#>  1958-09-01     117   404 360.7524
#>  1958-10-01     118   359 303.4957
#>         ---     ---   ---      ---
#>  1960-10-01     142   461 360.4986
#>  1960-11-01     143   390 340.1683
#>  1960-12-01     144   432 356.7078
# }