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Single Layer Neural Network. Calls forecast::nnetar() 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.nnetar")
lrn("fcst.nnetar")

Meta Information

  • Task type: “fcst”

  • Predict Types: “response”

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

  • Required Packages: mlr3, mlr3forecast, forecast

Parameters

IdTypeDefaultLevelsRange
puntyped--
Pinteger1\([0, \infty)\)
sizeinteger-\((-\infty, \infty)\)
repeatsinteger20\((-\infty, \infty)\)
lambdauntypedNULL-
scale.inputslogicalTRUETRUE, FALSE-
bootstraplogicalFALSETRUE, FALSE-
npathsinteger1000\([1, \infty)\)
innovuntypedNULL-

References

Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi:10.1017/cbo9780511812651 .

See also

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.ets, mlr_learners_fcst.tbats

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstNnetar$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.nnetar")
print(learner)
#> 
#> ── <LearnerFcstNnetar> (fcst.nnetar): Neural Network Time Series Forecasts ─────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • Predict Types: [response]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: exogenous, 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)
#> Series: as.ts(task) 
#> Model:  NNAR(1,1,2)[12] 
#> Call:   forecast::nnetar(y = as.ts(task), xreg = xreg)
#> 
#> Average of 20 networks, each of which is
#> a 2-2-1 network with 9 weights
#> options were - linear output units 
#> 
#> sigma^2 estimated as 132.2

# 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 
#> 2902.244