ARFIMA Forecast Learner

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

Id	Type	Default	Levels	Range
drange	untyped	c(0, 0.5)		-
estim	character	mle	mle, ls	-
lambda	untyped	NULL		-
biasadj	logical	FALSE	TRUE, FALSE	-
simulate	logical	FALSE	TRUE, FALSE	-
bootstrap	logical	FALSE	TRUE, FALSE	-
npaths	integer	5000		\([1, \infty)\)
max.p	integer	5		\([0, \infty)\)
max.q	integer	5		\([0, \infty)\)
max.order	integer	5		\([0, \infty)\)
start.p	integer	2		\([0, \infty)\)
start.q	integer	2		\([0, \infty)\)
ic	character	aicc	aicc, aic, bic	-
stepwise	logical	TRUE	TRUE, FALSE	-
nmodels	integer	94		\([0, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
approximation	logical	-	TRUE, FALSE	-
method	character	NULL	CSS-ML, ML, CSS	-
truncate	integer	NULL		\([1, \infty)\)
parallel	logical	FALSE	TRUE, FALSE	-
num.cores	integer	2		\([1, \infty)\)
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

Haslett J, Raftery AE (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 RJ, Khandakar Y (2008). “Automatic Time Series Forecasting: The forecast Package for R.” Journal of Statistical Software, 27(3), 1–22. doi:10.18637/jss.v027.i03 .

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.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.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 -> LearnerFcstArfima

Methods

Public methods

• LearnerFcstArfima$new()

• LearnerFcstArfima$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()

---

LearnerFcstArfima$new()

Creates a new instance of this R6 class.

Usage

LearnerFcstArfima$new()

---

LearnerFcstArfima$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: 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
#> 
#> Call:
#>   arfima(y = passengers) 
#> 
#> *** 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"         
#> 
#> $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 
#> 33706.15