Augmented Dynamic Adaptive Model Forecast Learner
Source:R/LearnerFcstAdam.R
mlr_learners_fcst.adam.RdADAM model.
Calls smooth::adam() from package smooth.
Dictionary
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():
Meta Information
Task type: “fcst”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”, “Date”
Required Packages: mlr3, mlr3forecast, smooth
Parameters
| Id | Type | Default | Levels |
| model | untyped | "ZXZ" | |
| lags | untyped | - | |
| orders | untyped | - | |
| constant | logical | FALSE | TRUE, FALSE |
| regressors | character | use | use, select, adapt |
| occurrence | character | none | none, auto, fixed, general, odds-ratio, inverse-odds-ratio, direct |
| distribution | character | default | default, dnorm, dlaplace, ds, dgnorm, dlnorm, dinvgauss, dgamma |
| loss | character | likelihood | likelihood, MSE, MAE, HAM, LASSO, RIDGE, MSEh, TMSE, GTMSE, MSCE |
| outliers | character | ignore | ignore, use, select |
| holdout | logical | FALSE | TRUE, FALSE |
| persistence | untyped | NULL | |
| phi | untyped | NULL | |
| initial | character | optimal | optimal, backcasting, complete |
| arma | untyped | NULL | |
| ic | character | AICc | AICc, AIC, BIC, BICc |
| bounds | character | usual | usual, admissible, none |
| silent | logical | TRUE | TRUE, FALSE |
References
Svetunkov I (2023). “Smooth forecasting with the smooth package in R.” 2301.01790, https://arxiv.org/abs/2301.01790.
Svetunkov, Ivan (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM), 1st edition. Chapman and Hall/CRC. doi:10.1201/9781003452652 , https://openforecast.org/adam/.
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.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 -> LearnerFcstAdam