ETS Forecast Learner

Exponential Smoothing State Space (ETS) model. Calls forecast::ets() 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.ets")
lrn("fcst.ets")

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
model	untyped	"ZZZ"		-
damped	logical	NULL	TRUE, FALSE	-
alpha	numeric	NULL		\((-\infty, \infty)\)
beta	numeric	NULL		\((-\infty, \infty)\)
gamma	numeric	NULL		\((-\infty, \infty)\)
phi	numeric	NULL		\((-\infty, \infty)\)
additive.only	logical	FALSE	TRUE, FALSE	-
lambda	untyped	NULL		-
biasadj	logical	FALSE	TRUE, FALSE	-
lower	untyped	c(rep.int(1e-04, 3), 0.8)		-
upper	untyped	c(rep.int(0.9999, 3), 0.98)		-
opt.crit	character	lik	lik, amse, mse, sigma, mae	-
nmse	integer	3		\([0, 30]\)
bounds	character	both	both, usual, admissible	-
ic	character	aicc	aicc, aic, bic	-
restrict	logical	TRUE	TRUE, FALSE	-
allow.multiplicative.trend	logical	FALSE	TRUE, FALSE	-
na.action	character	na.contiguous	na.contiguous, na.interp, na.fail	-
simulate	logical	FALSE	TRUE, FALSE	-
bootstrap	logical	FALSE	TRUE, FALSE	-
npaths	integer	5000		\([1, \infty)\)

References

Hyndman, R.J., Koehler, A.B., Snyder, R.D., Grose, S. (2002). “A state space framework for automatic forecasting using exponential smoothing methods.” International J. Forecasting, 18(3), 439–454.

Hyndman, R.J., Akram, Md., Archibald, B. (2008). “The admissible parameter space for exponential smoothing models.” Annals of Statistical Mathematics, 60(2), 407–426.

Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008). Forecasting with exponential smoothing: the state space approach. Springer-Verlag. http://www.exponentialsmoothing.net.

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.bats, mlr_learners_fcst.ces, mlr_learners_fcst.nnetar, mlr_learners_fcst.tbats

Super classes

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

Methods

Public methods

• LearnerFcstEts$new()

• LearnerFcstEts$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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Method new()

Creates a new instance of this R6 class.

Usage

LearnerFcstEts$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstEts$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.ets")
print(learner)
#> 
#> ── <LearnerFcstEts> (fcst.ets): ETS ────────────────────────────────────────────
#> • 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)
#> ETS(M,Ad,M) 
#> 
#> Call:
#> forecast::ets(y = as.ts(task))
#> 
#>   Smoothing parameters:
#>     alpha = 0.6938 
#>     beta  = 0.0321 
#>     gamma = 1e-04 
#>     phi   = 0.9789 
#> 
#>   Initial states:
#>     l = 120.1012 
#>     b = 1.8118 
#>     s = 0.9047 0.7971 0.9166 1.0554 1.191 1.2065
#>            1.0963 0.9774 0.9928 1.0383 0.9118 0.912
#> 
#>   sigma:  0.038
#> 
#>      AIC     AICc      BIC 
#> 846.1827 855.0658 892.3409 

# 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 
#> 2983.694