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, 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.

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.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerFcst -> LearnerFcstForecast -> LearnerFcstArfima

Methods

Inherited methods

`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:
#>   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")) 
#> 
#> *** 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

Dictionary

Meta Information

Parameters

References

See also

Super classes

Methods

Public methods

LearnerFcstArfima$new()

Usage

LearnerFcstArfima$clone()

Usage

Arguments

Examples

`LearnerFcstArfima$new()`

`LearnerFcstArfima$clone()`