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()
:
Meta Information
Task type: “fcst”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3forecast, forecast
Parameters
Id | Type | Default | Levels | Range |
p | untyped | - | - | |
P | integer | 1 | \([0, \infty)\) | |
size | integer | - | \((-\infty, \infty)\) | |
repeats | integer | 20 | \((-\infty, \infty)\) | |
lambda | untyped | NULL | - | |
scale.inputs | logical | TRUE | TRUE, FALSE | - |
bootstrap | logical | FALSE | TRUE, FALSE | - |
npaths | integer | 1000 | \([1, \infty)\) | |
innov | untyped | NULL | - |
References
Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi:10.1017/cbo9780511812651 .
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.
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.ets
,
mlr_learners_fcst.tbats
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> mlr3forecast::LearnerFcst
-> mlr3forecast::LearnerFcstForecast
-> LearnerFcstNnetar
Methods
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()
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