Create Lags of Target Variable — mlr_pipeops

Creates lagged versions of the target variable as new feature columns.

At train time the first rows of each series have no history for the requested lags. These incomplete rows are dropped (the autoregressive-fit convention), so the base learner never sees NA lags. A keyed series shorter than the largest lag is dropped entirely, with a warning.

At predict time lags are computed from the full series history.

Parameters

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

lags :: integer()
The lags to create.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpFcstLags

Methods

Inherited methods

`PipeOpFcstLags$new()`

Initializes a new instance of this Class.

Usage

PipeOpFcstLags$new(id = "fcst.lags", param_vals = list())

Arguments

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

`PipeOpFcstLags$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpFcstLags$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(mlr3pipelines)
task = tsk("airpassengers")
po = po("fcst.lags", lags = 1:3)
new_task = po$train(list(task))[[1L]]
new_task$head()
#>    passengers passengers_lag_1 passengers_lag_2 passengers_lag_3
#>         <num>            <num>            <num>            <num>
#> 1:        129              132              118              112
#> 2:        121              129              132              118
#> 3:        135              121              129              132
#> 4:        148              135              121              129
#> 5:        148              148              135              121
#> 6:        136              148              148              135