Exponential Smoothing State Space (ETS) model.
Calls forecast::ets() 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 |
| model | untyped | "ZZZ" | - | |
| damped | logical | NULL | TRUE, FALSE | - |
| alpha | numeric | NULL | \((-\infty, \infty)\) | |
| beta | numeric | NULL | \((-\infty, \infty)\) | |
| gamma | numeric | NULL | \((-\infty, \infty)\) | |
| phi | numeric | NULL | \((-\infty, \infty)\) | |
| additive.only | logical | FALSE | TRUE, FALSE | - |
| lambda | untyped | NULL | - | |
| biasadj | logical | FALSE | TRUE, FALSE | - |
| lower | untyped | c(rep.int(1e-04, 3), 0.8) | - | |
| upper | untyped | c(rep.int(0.9999, 3), 0.98) | - | |
| opt.crit | character | lik | lik, amse, mse, sigma, mae | - |
| nmse | integer | 3 | \([0, 30]\) | |
| bounds | character | both | both, usual, admissible | - |
| ic | character | aicc | aicc, aic, bic | - |
| restrict | logical | TRUE | TRUE, FALSE | - |
| allow.multiplicative.trend | logical | FALSE | TRUE, FALSE | - |
| simulate | logical | FALSE | TRUE, FALSE | - |
| bootstrap | logical | FALSE | TRUE, FALSE | - |
| npaths | integer | 5000 | \([1, \infty)\) |
References
Hyndman RJ, Koehler AB, Snyder RD, Grose S (2002). “A state space framework for automatic forecasting using exponential smoothing methods.” International Journal of Forecasting, 18(3), 439–454.
Hyndman RJ, Akram M, Archibald B (2008). “The admissible parameter space for exponential smoothing models.” Annals of the Institute of Statistical Mathematics, 60(2), 407–426.
Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008). Forecasting with exponential smoothing: the state space approach. Springer-Verlag. https://robjhyndman.com/expsmooth.
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.bats,
mlr_learners_fcst.ces,
mlr_learners_fcst.croston,
mlr_learners_fcst.elm,
mlr_learners_fcst.es,
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 -> LearnerFcstEts
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.ets")
print(learner)
#>
#> ── <LearnerFcstEts> (fcst.ets): ETS ────────────────────────────────────────────
#> • 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
#> ETS(M,Ad,M)
#>
#> Call:
#> ets(y = passengers)
#>
#> Smoothing parameters:
#> alpha = 0.6938
#> beta = 0.0321
#> gamma = 1e-04
#> phi = 0.9789
#>
#> Initial states:
#> l = 120.1012
#> b = 1.8118
#> s = 0.9047 0.7971 0.9166 1.0554 1.191 1.2065
#> 1.0963 0.9774 0.9928 1.0383 0.9118 0.912
#>
#> sigma: 0.038
#>
#> AIC AICc BIC
#> 846.1827 855.0658 892.3409
#>
#> $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
#> 2983.694