Augmented Dynamic Adaptive Model (ADAM) Forecast Learner 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”, “quantiles”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3forecast, smooth
Parameters
| Id | Type | Default | Levels | Range |
| model | untyped | "ZXZ" | - | |
| lags | untyped | - | - | |
| orders | untyped | list(ar = 0, i = 0, ma = 0, select = FALSE) | - | |
| 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 | numeric | NULL | \((-\infty, \infty)\) | |
| 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 | - |
| 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.
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.auto_gum,
mlr_learners_fcst.auto_msarima,
mlr_learners_fcst.auto_ssarima,
mlr_learners_fcst.bagged,
mlr_learners_fcst.bats,
mlr_learners_fcst.ces,
mlr_learners_fcst.croston,
mlr_learners_fcst.elm,
mlr_learners_fcst.es,
mlr_learners_fcst.ets,
mlr_learners_fcst.gum,
mlr_learners_fcst.holt_winters,
mlr_learners_fcst.mean,
mlr_learners_fcst.mlp,
mlr_learners_fcst.msarima,
mlr_learners_fcst.nnetar,
mlr_learners_fcst.prophet,
mlr_learners_fcst.random_walk,
mlr_learners_fcst.rlgt,
mlr_learners_fcst.sma,
mlr_learners_fcst.spline,
mlr_learners_fcst.ssarima,
mlr_learners_fcst.stlm,
mlr_learners_fcst.struct_ts,
mlr_learners_fcst.tbats,
mlr_learners_fcst.theta,
mlr_learners_fcst.tscount,
mlr_learners_fcst.tslm
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> LearnerFcst -> LearnerFcstSmooth -> LearnerFcstAdam
Methods
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()
Examples
# Define the Learner and set parameter values
learner = lrn("fcst.adam")
print(learner)
#>
#> ── <LearnerFcstAdam> (fcst.adam): ADAM ─────────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and smooth
#> • Predict Types: [response] and quantiles
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: exogenous, featureless, and missings
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# 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)
#> $model
#> Time elapsed: 0.16 seconds
#> Model estimated using fn() function: ETS(MAM)
#> With backcasting initialisation
#> Distribution assumed in the model: Gamma
#> Loss function type: likelihood; Loss function value: 328.324
#> Persistence vector g:
#> alpha beta gamma
#> 0.5507 0.0095 0.1489
#>
#> Sample size: 96
#> Number of estimated parameters: 4
#> Number of degrees of freedom: 92
#> Information criteria:
#> AIC AICc BIC BICc
#> 664.6481 665.0877 674.9055 675.9086
#>
#> $row_ids
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#> [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
#> [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
#>
#> $max_index
#> [1] "1956-12-01"
#>
# Importance method
if ("importance" %in% learner$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
#> 839.4905