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ARFIMA model. Calls forecast::arfima() from package forecast.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("fcst.arfima")
lrn("fcst.arfima")

Meta Information

  • Task type: “fcst”

  • Predict Types: “response”, “quantiles”

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

  • Required Packages: mlr3, mlr3forecast, forecast

Parameters

IdTypeDefaultLevels
drangeuntypedc(0, 0.5)
estimcharactermlemle, ls
lambdauntypedNULL
orderuntypedc(0L, 0L, 0L)
seasonaluntypedc(0L, 0L, 0L)
include.meanlogicalTRUETRUE, FALSE
include.driftlogicalFALSETRUE, FALSE
biasadjlogicalFALSETRUE, FALSE
methodcharacterCSS-MLCSS-ML, ML, CSS

References

Haslett, John, Raftery, E A (1989). “Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 38(1), 1–21.

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.

See also

Other Learner: LearnerFcst, mlr_learners_fcst.arima, mlr_learners_fcst.auto_arima, mlr_learners_fcst.bats, 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

LearnerFcstArfima$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.