Vector Autoregression Forecast Learner

Vector autoregressive model Calls vars::VAR from package vars.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("forecast.VAR")
lrn("forecast.VAR")

Meta Information

• Task type: “forecast”

• Predict Types: “response”, “se”

• Feature Types: “numeric”

• Required Packages: mlr3, vars

Parameters

Id	Type	Default	Range
p	integer	1	\([0, \infty)\)
lag.max	integer	NULL	\([1, \infty)\)
season	integer	NULL	\([1, \infty)\)

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3learners for a solid collection of essential learners.

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Package mlr3viz for some generic visualizations.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: LearnerForecast, mlr_learners_regr.arima, mlr_learners_regr.auto_arima, mlr_learners_regr.average

Super classes

mlr3::Learner -> mlr3temporal::LearnerForecast -> LearnerVAR

Methods

Public methods

• LearnerRegrForecastVAR$new()

• LearnerRegrForecastVAR$forecast()

• LearnerRegrForecastVAR$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3temporal::LearnerForecast$fitted_values()
• mlr3temporal::LearnerForecast$predict()
• mlr3temporal::LearnerForecast$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrForecastVAR$new()

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Method forecast()

Returns forecasts after the last training instance.

Usage

LearnerRegrForecastVAR$forecast(h = 10, task, new_data = NULL)

Arguments

h

(numeric(1))
Number of steps ahead to forecast. Default is 10.

task

(Task).

new_data

(data.frame())
New data to predict on.

Returns

Prediction.

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrForecastVAR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("forecast.VAR")
print(learner)
#> <LearnerVAR:forecast.VAR>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, vars
#> * Predict Types:  [response], se
#> * Feature Types: numeric
#> * Properties: exogenous, missings, multivariate

# available parameters:
learner$param_set$ids()
#> [1] "p"       "lag.max" "season"