ARIMA
mlr_learners_fcst.arima.RdARIMA model.
Calls forecast::Arima() from package forecast.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
Meta Information
Task type: “fcst”
Predict Types: “response”, “quantiles”
Feature Types: “logical”, “integer”, “numeric”, “Date”
Required Packages: mlr3, mlr3forecast, forecast
Parameters
| Id | Type | Default | Levels |
| order | untyped | c(0L, 0L, 0L) | |
| seasonal | untyped | c(0L, 0L, 0L) | |
| include.mean | logical | TRUE | TRUE, FALSE |
| include.drift | logical | FALSE | TRUE, FALSE |
| biasadj | logical | FALSE | TRUE, FALSE |
| method | character | CSS-ML | CSS-ML, ML, CSS |
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.
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.arfima,
mlr_learners_fcst.auto_arima,
mlr_learners_fcst.bats,
mlr_learners_fcst.ets,
mlr_learners_fcst.tbats
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> mlr3forecast::LearnerFcst -> mlr3forecast::LearnerFcstForecast -> LearnerFcstArima