mlr_learners_regr.average: Average Forecast Learner

In mlr-org/mlr3forecasting: Forecasting for 'mlr3'

Average Forecast Learner

Description

A model based on average values Calls base::mean from package base.

Dictionary

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

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

Meta Information

• Task type: “forecast”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, 'base'

Parameters

Empty ParamSet

Super classes

mlr3::Learner -> mlr3temporal::LearnerForecast -> LearnerRegrForecastAverage

Methods

Public methods

• LearnerRegrForecastAverage$new()

• LearnerRegrForecastAverage$forecast()

• LearnerRegrForecastAverage$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

LearnerRegrForecastAverage$new()

---

Method forecast()

Returns forecasts after the last training instance.

Usage

LearnerRegrForecastAverage$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

LearnerRegrForecastAverage$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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.VAR, mlr_learners_regr.arima, mlr_learners_regr.auto_arima

Examples

learner = mlr3::lrn("forecast.average")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3forecasting documentation built on June 29, 2023, 11:57 p.m.