Spline Forecast Learner

Cubic spline stochastic model. Calls forecast::spline_model() from package forecast.

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.spline")
lrn("fcst.spline")

Meta Information

• Task type: “fcst”

• Predict Types: “response”, “quantiles”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3forecast, forecast

Parameters

Id	Type	Default	Levels	Range
method	character	gcv	gcv, mle	-
lambda	untyped	NULL		-
biasadj	logical	FALSE	TRUE, FALSE	-
simulate	logical	FALSE	TRUE, FALSE	-
bootstrap	logical	FALSE	TRUE, FALSE	-
npaths	integer	5000		\([1, \infty)\)

References

Hyndman, J R, King, L M, Pitrun, Ivet, Billah, Baki (2005). “Local linear forecasts using cubic smoothing splines.” Australian & New Zealand Journal of Statistics, 47(1), 87–99.

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_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.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 -> LearnerFcstSpline

Methods

Public methods

• LearnerFcstSpline$new()

• LearnerFcstSpline$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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LearnerFcstSpline$new()

Creates a new instance of this R6 class.

Usage

LearnerFcstSpline$new()

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LearnerFcstSpline$clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstSpline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.spline")
print(learner)
#> 
#> ── <LearnerFcstSpline> (fcst.spline): Spline ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • Predict Types: [response] and quantiles
#> • Feature Types: logical, integer, and numeric
#> • 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
#> Cubic spline stochastic model
#> Call: fn(y = 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 parameter: 0.08847 
#> 
#> $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 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 52 53 54 55 56 57 58
#> [59] 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 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 
#> 570230.1