Auto ARIMA Forecast Learner

Auto ARIMA model. Calls forecast::auto.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.auto_arima")
lrn("fcst.auto_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
d	integer	NA		\([0, \infty)\)
D	integer	NA		\([0, \infty)\)
max.p	integer	5		\([0, \infty)\)
max.q	integer	5		\([0, \infty)\)
max.P	integer	2		\([0, \infty)\)
max.Q	integer	2		\([0, \infty)\)
max.order	integer	5		\([0, \infty)\)
max.d	integer	2		\([0, \infty)\)
max.D	integer	1		\([0, \infty)\)
start.p	integer	2		\([0, \infty)\)
start.q	integer	2		\([0, \infty)\)
start.P	integer	2		\([0, \infty)\)
start.Q	integer	2		\([0, \infty)\)
stationary	logical	FALSE	TRUE, FALSE	-
seasonal	logical	FALSE	TRUE, FALSE	-
ic	character	aicc	aicc, aic, bic	-
stepwise	logical	FALSE	TRUE, FALSE	-
nmodels	integer	94		\([0, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
approximation	untyped	-		-
method	untyped	NULL		-
truncate	untyped	NULL		-
test	character	kpss	kpss, adf, pp	-
test.args	untyped	list()		-
seasonal.test	character	seas	seas, ocsb, hegy, ch	-
seasonal.test.args	untyped	list()		-
allowdrift	logical	TRUE	TRUE, FALSE	-
allowmean	logical	TRUE	TRUE, FALSE	-
biasadj	logical	FALSE	TRUE, FALSE	-
parallel	logical	FALSE	TRUE, FALSE	-
num.cores	integer	2		\([1, \infty)\)
include.mean	logical	TRUE	TRUE, FALSE	-
include.drift	logical	FALSE	TRUE, FALSE	-
include.constant	logical	FALSE	TRUE, FALSE	-
lambda	untyped	NULL		-
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, J. R, Khandakar, Yeasmin (2008). “Automatic Time Series Forecasting: The forecast Package for R.” Journal of Statistical Software, 27(3), 1–22. doi:10.18637/jss.v027.i03 , https://www.jstatsoft.org/index.php/jss/article/view/v027i03.

Wang, Xiaozhe, Smith, Kate, Hyndman, Rob (2006). “Characteristic-based clustering for time series data.” Data Mining and Knowledge Discovery, 13, 335–364.

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_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 -> LearnerFcstAutoArima

Methods

Public methods

• LearnerFcstAutoArima$new()

• LearnerFcstAutoArima$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

LearnerFcstAutoArima$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstAutoArima$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.auto_arima")
print(learner)
#> 
#> ── <LearnerFcstAutoArima> (fcst.auto_arima): Auto 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(1,1,0)(1,1,0)[12] 
#> 
#> Coefficients:
#>           ar1     sar1
#>       -0.2250  -0.2274
#> s.e.   0.1076   0.1081
#> 
#> sigma^2 = 92.5:  log likelihood = -304.98
#> AIC=615.97   AICc=616.27   BIC=623.22

# 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 
#> 700.7478