ARIMA Forecast Learner

Autoregressive Integrated Moving Average Forecast (ARIMA) model. Calls forecast::Arima() 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.arima")
lrn("fcst.arima")

Meta Information

• Task type: “fcst”

• Predict Types: “response”, “quantiles”

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

• Required Packages: mlr3, mlr3forecast, forecast

Parameters

Id	Type	Default	Levels	Range
order	untyped	c(0L, 0L, 0L)		-
seasonal	untyped	c(0L, 0L, 0L)		-
include.mean	logical	TRUE	TRUE, FALSE	-
include.drift	logical	FALSE	TRUE, FALSE	-
include.constant	logical	FALSE	TRUE, FALSE	-
lambda	untyped	NULL		-
biasadj	logical	FALSE	TRUE, FALSE	-
method	character	CSS-ML	CSS-ML, ML, CSS	-
bootstrap	logical	FALSE	TRUE, FALSE	-
npaths	integer	5000		\([1, \infty)\)
transform.pars	logical	TRUE	TRUE, FALSE	-
fixed	untyped	NULL		-
init	untyped	NULL		-
SSinit	character	Gardner1980	Gardner1980, Rossignol2011	-
n.cond	integer	-		\([1, \infty)\)
optim.method	character	BFGS	Nelder-Mead, BFGS, CG, L-BFGS-B, SANN, Brent	-
optim.control	untyped	list()		-
kappa	numeric	1e+06		\((-\infty, \infty)\)

References

Hyndman, R.J., Athanasopoulos, G. (2018). Forecasting: principles and practice, 2nd edition. OTexts, Melbourne, Australia. https://OTexts.com/fpp2/.

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.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.nnetar, mlr_learners_fcst.tbats

Super classes

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

Methods

Public methods

• LearnerFcstArima$new()

• LearnerFcstArima$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

LearnerFcstArima$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstArima$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.arima")
print(learner)
#> 
#> ── <LearnerFcstArima> (fcst.arima): ARIMA ──────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • Predict Types: [response] and quantiles
#> • 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) 
#> ARIMA(0,0,0) with non-zero mean 
#> 
#> Coefficients:
#>           mean
#>       213.7083
#> s.e.    7.3018
#> 
#> sigma^2 = 5172:  log likelihood = -546.17
#> AIC=1096.33   AICc=1096.46   BIC=1101.46

# 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 
#> 45942.01