Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components
(BATS) model.
Calls forecast::bats() from package forecast.
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”, “character”, “factor”, “ordered”, “POSIXct”, “Date”
Required Packages: mlr3, mlr3forecast, forecast
Parameters
| Id | Type | Default | Levels | Range |
| use.box.cox | logical | NULL | TRUE, FALSE | - |
| use.trend | logical | NULL | TRUE, FALSE | - |
| use.damped.trend | logical | NULL | TRUE, FALSE | - |
| seasonal.periods | untyped | NULL | - | |
| use.arma.errors | logical | TRUE | TRUE, FALSE | - |
| use.parallel | logical | - | TRUE, FALSE | - |
| num.cores | integer | 2 | \([1, \infty)\) | |
| bc.lower | numeric | 0 | \((-\infty, \infty)\) | |
| bc.upper | numeric | 1 | \((-\infty, \infty)\) | |
| biasadj | logical | FALSE | TRUE, FALSE | - |
References
De Livera, A.M., Hyndman, R.J., Snyder, R.D. (2011). “Forecasting time series with complex seasonal patterns using exponential smoothing.” Journal of the American Statistical Association, 106(496), 1513–1527.
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.adam,
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.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 -> LearnerFcstForecast -> LearnerFcstBats
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.bats")
print(learner)
#>
#> ── <LearnerFcstBats> (fcst.bats): BATS ─────────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • Predict Types: [response] and quantiles
#> • Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct, and Date
#> • Encapsulation: none (fallback: -)
#> • Properties: 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
#> BATS(0.001, {0,0}, 1, {12})
#>
#> Call: fn(y = c(112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104,
#> 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140,
#> 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171,
#> 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196,
#> 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235,
#> 227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269,
#> 270, 315, 364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318,
#> 374, 413, 405, 355, 306, 271, 306))
#>
#> Parameters
#> Lambda: 0.001323
#> Alpha: 0.7720669
#> Beta: 0.04383733
#> Damping Parameter: 1
#> Gamma Values: -0.07671992
#>
#> Seed States:
#> [,1]
#> [1,] 4.737291314
#> [2,] -0.007076601
#> [3,] -0.096905454
#> [4,] -0.222177523
#> [5,] -0.080521101
#> [6,] 0.060712206
#> [7,] 0.181980666
#> [8,] 0.194969610
#> [9,] 0.099418764
#> [10,] -0.013829347
#> [11,] 0.003219389
#> [12,] 0.046487603
#> [13,] -0.086452309
#> [14,] -0.086797324
#> attr(,"lambda")
#> [1] 0.001323467
#>
#> Sigma: 0.03550602
#> AIC: 851.3119
#>
#> $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
#> 1484.959