Autoregressive Integrated Moving Average Forecast (ARIMA) model.
Calls forecast::Arima() 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”
Required Packages: mlr3, mlr3forecast, forecast
Parameters
| Id | Type | Default | Levels | Range |
| order | untyped | c(0L, 0L, 0L) | - | |
| seasonal | untyped | c(0L, 0L, 0L) | - | |
| include.mean | logical | TRUE | TRUE, FALSE | - |
| include.drift | logical | FALSE | TRUE, FALSE | - |
| include.constant | logical | FALSE | TRUE, FALSE | - |
| lambda | untyped | NULL | - | |
| biasadj | logical | FALSE | TRUE, FALSE | - |
| method | character | CSS-ML | CSS-ML, ML, CSS | - |
| simulate | logical | FALSE | TRUE, FALSE | - |
| bootstrap | logical | FALSE | TRUE, FALSE | - |
| npaths | integer | 5000 | \([1, \infty)\) | |
| transform.pars | logical | TRUE | TRUE, FALSE | - |
| fixed | untyped | NULL | - | |
| init | untyped | NULL | - | |
| SSinit | character | Gardner1980 | Gardner1980, Rossignol2011 | - |
| n.cond | integer | - | \([1, \infty)\) | |
| optim.method | character | BFGS | Nelder-Mead, BFGS, CG, L-BFGS-B, SANN, Brent | - |
| optim.control | untyped | list() | - | |
| kappa | numeric | 1e+06 | \((-\infty, \infty)\) |
References
Hyndman, R.J., Athanasopoulos, G. (2018). Forecasting: principles and practice, 2nd edition. OTexts, Melbourne, Australia. https://OTexts.com/fpp2/.
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.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 -> LearnerFcstForecast -> LearnerFcstArima
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.arima")
print(learner)
#>
#> ── <LearnerFcstArima> (fcst.arima): ARIMA ──────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and forecast
#> • 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
#> Series: 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")
#> ARIMA(0,0,0) with non-zero mean
#>
#> Coefficients:
#> mean
#> 213.7083
#> s.e. 7.3018
#>
#> sigma^2 = 5172: log likelihood = -546.17
#> AIC=1096.33 AICc=1096.46 BIC=1101.46
#>
#> $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
#> 45942.01