BATS Forecast Learner

Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS) model. Calls forecast::bats() from package forecast.

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.bats")
lrn("fcst.bats")

Meta Information

• Task type: “fcst”

• Predict Types: “response”, “quantiles”

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

• Required Packages: mlr3, mlr3forecast, forecast

Parameters

Id	Type	Default	Levels	Range
use.box.cox	logical	NULL	TRUE, FALSE	-
use.trend	logical	NULL	TRUE, FALSE	-
use.damped.trend	logical	NULL	TRUE, FALSE	-
seasonal.periods	untyped	NULL		-
use.arma.errors	logical	TRUE	TRUE, FALSE	-
use.parallel	untyped	-		-
num.cores	integer	2		\([1, \infty)\)
bc.lower	numeric	0		\((-\infty, \infty)\)
bc.upper	numeric	1		\((-\infty, \infty)\)
biasadj	logical	FALSE	TRUE, FALSE	-

References

De Livera, A.M., Hyndman, R.J., Snyder &, D. R (2011). “Forecasting time series with complex seasonal patterns using exponential smoothing.” Journal of the American Statistical Association, 106(496), 1513–1527.

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.ces, mlr_learners_fcst.croston, mlr_learners_fcst.ets, mlr_learners_fcst.nnetar, mlr_learners_fcst.random_walk, mlr_learners_fcst.spline, mlr_learners_fcst.tbats, mlr_learners_fcst.theta

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> mlr3forecast::LearnerFcst -> mlr3forecast::LearnerFcstForecast -> LearnerFcstBats

Methods

Public methods

• LearnerFcstBats$new()

• LearnerFcstBats$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerFcstBats$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstBats$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# 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)
#> BATS(0.001, {0,0}, 1, {12})
#> 
#> Call: forecast::bats(y = as.ts(task))
#> 
#> Parameters
#>   Lambda: 0.001323
#>   Alpha: 0.7720669
#>   Beta: 0.04383733
#>   Damping Parameter: 1
#>   Gamma Values: -0.07671992
#> 
#> Seed States:
#>               [,1]
#>  [1,]  4.737291314
#>  [2,] -0.007076601
#>  [3,] -0.096905454
#>  [4,] -0.222177523
#>  [5,] -0.080521101
#>  [6,]  0.060712206
#>  [7,]  0.181980666
#>  [8,]  0.194969610
#>  [9,]  0.099418764
#> [10,] -0.013829347
#> [11,]  0.003219389
#> [12,]  0.046487603
#> [13,] -0.086452309
#> [14,] -0.086797324
#> attr(,"lambda")
#> [1] 0.001323467
#> 
#> Sigma: 0.03550602
#> AIC: 851.3119

# 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 
#> 1484.959