Auto ARIMA
mlr_learners_fcst.auto_arima.RdAuto ARIMA model.
Calls forecast::auto.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 | Range |
| d | integer | NA | \([0, \infty)\) | |
| D | integer | NA | \([0, \infty)\) | |
| max.q | integer | 5 | \([0, \infty)\) | |
| max.p | 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 | \([0, \infty)\) | |
| stepwise | logical | FALSE | TRUE, FALSE | - |
| allowdrift | logical | TRUE | TRUE, FALSE | - |
| seasonal | logical | FALSE | TRUE, FALSE | - |
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.
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.arima,
mlr_learners_fcst.bats,
mlr_learners_fcst.ets,
mlr_learners_fcst.tbats
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> mlr3forecast::LearnerFcst -> mlr3forecast::LearnerFcstForecast -> LearnerFcstAutoArima