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()
:
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 | - |
d | integer | NA | \([0, \infty)\) | |
D | integer | NA | \([0, \infty)\) | |
max.p | integer | 5 | \([0, \infty)\) | |
max.q | integer | 5 | \([0, \infty)\) | |
max.order | integer | 5 | \([0, \infty)\) | |
max.d | integer | 2 | \([0, \infty)\) | |
max.D | integer | 1 | \([0, \infty)\) | |
start.p | integer | 2 | \([0, \infty)\) | |
start.q | integer | 2 | \([0, \infty)\) | |
start.P | integer | 2 | \([0, \infty)\) | |
start.Q | integer | 2 | \([0, \infty)\) | |
seasonal | logical | FALSE | TRUE, FALSE | - |
ic | character | aicc | aicc, aic, bic | - |
stepwise | logical | FALSE | TRUE, FALSE | - |
nmodels | integer | 94 | \([0, \infty)\) | |
trace | logical | FALSE | TRUE, FALSE | - |
approximation | untyped | - | - | |
method | untyped | NULL | - | |
truncate | untyped | NULL | - | |
test | character | kpss | kpss, adf, pp | - |
test.args | untyped | list() | - | |
seasonal.test | character | seas | seas, ocsb, hegy, ch | - |
seasonal.test.args | untyped | list() | - | |
allowdrift | logical | TRUE | TRUE, FALSE | - |
allowmean | logical | TRUE | TRUE, FALSE | - |
parallel | logical | FALSE | TRUE, FALSE | - |
num.cores | integer | 2 | \([1, \infty)\) | |
include.mean | logical | TRUE | TRUE, FALSE | - |
include.drift | logical | FALSE | TRUE, FALSE | - |
include.constant | logical | FALSE | TRUE, FALSE | - |
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.
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.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
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> mlr3forecast::LearnerFcst
-> mlr3forecast::LearnerFcstForecast
-> LearnerFcstArfima
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.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