Holt-Winters exponential smoothing with optional trend and additive or multiplicative seasonal component.
Smoothing parameters are estimated by minimizing the squared one-step prediction error.
Calls stats::HoltWinters() from package stats and forecasts via forecast::forecast().
Setting beta = FALSE fits a simple exponential smoothing model (no trend).
Setting gamma = FALSE fits a non-seasonal model.
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 |
| alpha | numeric | NULL | \([0, 1]\) | |
| beta | numeric | NULL | \([0, 1]\) | |
| gamma | numeric | NULL | \([0, 1]\) | |
| seasonal | character | additive | additive, multiplicative | - |
| start.periods | integer | 2 | \([2, \infty)\) | |
| l.start | numeric | NULL | \((-\infty, \infty)\) | |
| b.start | numeric | NULL | \((-\infty, \infty)\) | |
| s.start | untyped | NULL | - | |
| optim.start | untyped | c(alpha = 0.3, beta = 0.1, gamma = 0.1) | - | |
| optim.control | untyped | list() | - | |
| lambda | untyped | NULL | - | |
| biasadj | logical | FALSE | TRUE, FALSE | - |
References
Holt, C. C (2004). “Forecasting seasonals and trends by exponentially weighted moving averages.” International Journal of Forecasting, 20(1), 5–10. doi:10.1016/j.ijforecast.2003.09.015 .
Winters, R. P (1960). “Forecasting Sales by Exponentially Weighted Moving Averages.” Management Science, 6(3), 324–342. doi:10.1287/mnsc.6.3.324 .
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.ets,
mlr_learners_fcst.gum,
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 -> LearnerFcstHoltWinters
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()
LearnerFcstHoltWinters$new()
Creates a new instance of this R6 class.
Usage
LearnerFcstHoltWinters$new()Examples
# Define the Learner and set parameter values
learner = lrn("fcst.holt_winters")
print(learner)
#>
#> ── <LearnerFcstHoltWinters> (fcst.holt_winters): Holt-Winters ──────────────────
#> • 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
#> Holt-Winters exponential smoothing with trend and additive seasonal component.
#>
#> Call:
#> fn(x = structure(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), tsp = c(1, 8.91666666666667, 12), class = "ts"))
#>
#> Smoothing parameters:
#> alpha: 0.2287362
#> beta : 0.05161695
#> gamma: 1
#>
#> Coefficients:
#> [,1]
#> a 333.398461
#> b 2.924446
#> s1 -14.893284
#> s2 -25.307089
#> s3 10.725122
#> s4 4.590948
#> s5 6.911731
#> s6 57.136965
#> s7 91.836674
#> s8 76.991025
#> s9 23.706032
#> s10 -26.033269
#> s11 -62.070036
#> s12 -27.398461
#>
#> $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
#> 678.3176