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ARFIMA model. Calls forecast::arfima() 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.arfima")
lrn("fcst.arfima")

Meta Information

  • Task type: “fcst”

  • Predict Types: “response”, “quantiles”

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

  • Required Packages: mlr3, mlr3forecast, forecast

Parameters

IdTypeDefaultLevelsRange
drangeuntypedc(0, 0.5)-
estimcharactermlemle, ls-
lambdauntypedNULL-
biasadjlogicalFALSETRUE, FALSE-
dintegerNA\([0, \infty)\)
DintegerNA\([0, \infty)\)
max.pinteger5\([0, \infty)\)
max.qinteger5\([0, \infty)\)
max.orderinteger5\([0, \infty)\)
max.dinteger2\([0, \infty)\)
max.Dinteger1\([0, \infty)\)
start.pinteger2\([0, \infty)\)
start.qinteger2\([0, \infty)\)
start.Pinteger2\([0, \infty)\)
start.Qinteger2\([0, \infty)\)
seasonallogicalFALSETRUE, FALSE-
iccharacteraiccaicc, aic, bic-
stepwiselogicalFALSETRUE, FALSE-
nmodelsinteger94\([0, \infty)\)
tracelogicalFALSETRUE, FALSE-
approximationuntyped--
methoduntypedNULL-
truncateuntypedNULL-
testcharacterkpsskpss, adf, pp-
test.argsuntypedlist()-
seasonal.testcharacterseasseas, ocsb, hegy, ch-
seasonal.test.argsuntypedlist()-
allowdriftlogicalTRUETRUE, FALSE-
allowmeanlogicalTRUETRUE, FALSE-
parallellogicalFALSETRUE, FALSE-
num.coresinteger2\([1, \infty)\)
include.meanlogicalTRUETRUE, FALSE-
include.driftlogicalFALSETRUE, FALSE-
include.constantlogicalFALSETRUE, FALSE-
transform.parslogicalTRUETRUE, FALSE-
fixeduntypedNULL-
inituntypedNULL-
SSinitcharacterGardner1980Gardner1980, Rossignol2011-
n.condinteger-\([1, \infty)\)
optim.methodcharacterBFGSNelder-Mead, BFGS, CG, L-BFGS-B, SANN, Brent-
optim.controluntypedlist()-
kappanumeric1e+06\((-\infty, \infty)\)

References

Haslett, John, Raftery, E A (1989). “Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 38(1), 1–21.

Hyndman, J. R, Khandakar, Yeasmin (2008). “Automatic Time Series Forecasting: The forecast Package for R.” Journal of Statistical Software, 27(3), 1–22. doi:10.18637/jss.v027.i03 , https://www.jstatsoft.org/index.php/jss/article/view/v027i03.

See also

Other Learner: LearnerFcst, mlr_learners_fcst.adam, mlr_learners_fcst.arima, mlr_learners_fcst.auto_adam, mlr_learners_fcst.auto_arima, mlr_learners_fcst.auto_ces, mlr_learners_fcst.bats, mlr_learners_fcst.ces, mlr_learners_fcst.ets, mlr_learners_fcst.nnetar, mlr_learners_fcst.tbats

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerFcstArfima$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("fcst.arfima")
print(learner)
#> 
#> ── <LearnerFcstArfima> (fcst.arfima): ARFIMA ───────────────────────────────────
#> • 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'

# 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)
#> 
#> Call:
#>   forecast::arfima(y = as.ts(task), xreg = xreg) 
#> 
#> *** Warning during (fracdf) fit: C fracdf() optimization failure
#>  
#> *** Warning during (fdcov) fit: unable to compute correlation matrix; maybe change 'h'
#> 
#> Coefficients:
#>          d     ar.ar1     ar.ar2     ma.ma1 
#>  0.3093729  0.1950795  0.4392743 -0.8524371 
#> sigma[eps] = 18.21006 
#> a list with components:
#>  [1] "log.likelihood"  "n"               "msg"             "d"              
#>  [5] "ar"              "ma"              "covariance.dpq"  "fnormMin"       
#>  [9] "sigma"           "stderror.dpq"    "correlation.dpq" "h"              
#> [13] "d.tol"           "M"               "hessian.dpq"     "length.w"       
#> [17] "residuals"       "fitted"          "call"            "x"              
#> [21] "series"         

# 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 
#> 33706.15