Auto ADAM Forecast Learner

Auto Augmented Dynamic Adaptive Model (ADAM) model. Calls smooth::auto.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():

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

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	-
regressors	character	use	use, select, adapt
occurrence	character	none	none, auto, fixed, general, odds-ratio, inverse-odds-ratio, direct
distribution	character	dnorm	dnorm, dlaplace, ds, dgnorm, dlnorm, dinvgauss, dgamma
outliers	character	ignore	ignore, use, select
holdout	logical	FALSE	TRUE, FALSE
persistence	untyped	NULL
phi	untyped	NULL
initial	character	backcasting	backcasting, optimal, two-stage, complete
arma	untyped	NULL
ic	character	AICc	AICc, AIC, BIC, BICc
bounds	character	usual	usual, admissible, none
silent	logical	TRUE	TRUE, FALSE
parallel	logical	FALSE	TRUE, FALSE
ets	character	conventional	conventional, adam

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.

• 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_arima, mlr_learners_fcst.auto_ces, mlr_learners_fcst.bats, mlr_learners_fcst.ces, mlr_learners_fcst.croston, mlr_learners_fcst.ets, mlr_learners_fcst.nnetar, mlr_learners_fcst.random_walk, mlr_learners_fcst.spline, mlr_learners_fcst.tbats, mlr_learners_fcst.theta

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> mlr3forecast::LearnerFcst -> LearnerFcstAutoAdam

Methods

Public methods

• LearnerFcstAutoAdam$new()

• LearnerFcstAutoAdam$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

LearnerFcstAutoAdam$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstAutoAdam$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.auto_adam")
print(learner)
#> 
#> ── <LearnerFcstAutoAdam> (fcst.auto_adam): Auto ADAM ───────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and smooth
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct, and Date
#> • Encapsulation: none (fallback: -)
#> • Properties: 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)
#> Time elapsed: 3.65 seconds
#> Model estimated using auto.adam() function: ETS(MAM)
#> With backcasting initialisation
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 327.5537
#> Persistence vector g:
#>  alpha   beta  gamma 
#> 0.5640 0.0000 0.1468 
#> 
#> Sample size: 96
#> Number of estimated parameters: 4
#> Number of degrees of freedom: 92
#> Information criteria:
#>      AIC     AICc      BIC     BICc 
#> 663.1073 663.5469 673.3647 674.3679 

# 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 
#> 1233.255