Bagged Model Forecast Learner

Bootstrap-aggregated forecasts. The series is resampled via the Box-Cox/Loess moving block bootstrap of Bergmeir, Hyndman, and Benítez and fn is fit on each replicate; the forecast averages across the ensemble. Calls forecast::baggedModel() from package forecast.

fn is the model-fitting function applied to each bootstrap replicate (defaults to forecast::ets()). Any function returning an object compatible with forecast::forecast() may be passed, e.g. forecast::auto.arima() or forecast::Arima() with fixed orders via a wrapper. The number of bootstrap replicates is controlled by num, and block_size configures the moving block length in forecast::bld.mbb.bootstrap(). Prediction intervals from forecast::forecast.baggedModel() are the empirical bootstrap range (not configurable by level), so only "response" is offered as a predict_type.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("fcst.bagged")
lrn("fcst.bagged")

Meta Information

• Task type: “fcst”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3forecast, forecast

Parameters

Id	Type	Default	Range
fn	untyped	-	-
num	integer	100	\([1, \infty)\)
block_size	integer	NULL	\([1, \infty)\)

References

Bergmeir, Christoph, Hyndman, J R, Benítez, M J (2016). “Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation.” International Journal of Forecasting, 32(2), 303–312. doi:10.1016/j.ijforecast.2015.07.002 .

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3learners for a solid collection of essential learners.

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::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: LearnerFcst, mlr_learners_fcst.adam, mlr_learners_fcst.arfima, mlr_learners_fcst.arima, mlr_learners_fcst.auto_adam, mlr_learners_fcst.auto_arima, mlr_learners_fcst.auto_ces, mlr_learners_fcst.auto_gum, mlr_learners_fcst.auto_msarima, mlr_learners_fcst.bats, mlr_learners_fcst.ces, mlr_learners_fcst.croston, mlr_learners_fcst.ets, mlr_learners_fcst.gum, mlr_learners_fcst.holt_winters, mlr_learners_fcst.mean, mlr_learners_fcst.msarima, mlr_learners_fcst.nnetar, mlr_learners_fcst.prophet, mlr_learners_fcst.random_walk, mlr_learners_fcst.sma, mlr_learners_fcst.spline, mlr_learners_fcst.stlm, mlr_learners_fcst.struct_ts, mlr_learners_fcst.tbats, mlr_learners_fcst.theta, mlr_learners_fcst.tscount, mlr_learners_fcst.tslm

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerFcst -> LearnerFcstForecast -> LearnerFcstBaggedModel

Methods

Public methods

• LearnerFcstBaggedModel$new()

• LearnerFcstBaggedModel$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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LearnerFcstBaggedModel$new()

Creates a new instance of this R6 class.

Usage

LearnerFcstBaggedModel$new()

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LearnerFcstBaggedModel$clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstBaggedModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.bagged")
print(learner)
#> 
#> ── <LearnerFcstBaggedModel> (fcst.bagged): Bagged Model ────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct, and Date
#> • Encapsulation: none (fallback: -)
#> • Properties: featureless and missings
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

# Define a Task
task = tsk("airpassengers")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# Print the model
print(learner$model)
#> Series: y 
#> Model:  baggedModel 
#> Call:   forecast::baggedModel(y = y, bootstrapped_series = bootstrapped_series)

# Importance method
if ("importance" %in% learner$properties) print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 1265.878