This learner supports autoregressive fractionally integrated moving average and various flavors of generalized autoregressive conditional heteroskedasticity models for univariate time-series.
Lrnr_rugarch
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
variance.model
List containing variance model specification.
This includes model, GARCH order, submodel, external regressors and
variance tageting. Refer to ugarchspec
for more information.
mean.model
List containing the mean model specification. This
includes ARMA model, whether the mean should be included, and external
regressors among others. Refer to ugarchspec
for more
information.
distribution.model="norm"
Conditional density to use for the innovations.
start.pars=list()
List of staring parameters for the optimization routine.
fixed.pars=list()
List of parameters which are to be kept fixed during the optimization.
n.ahead=NULL
The forecast horizon.
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_bartMachine
, Lrnr_base
,
Lrnr_bilstm
, Lrnr_condensier
,
Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
,
Lrnr_glm_fast
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_independent_binomial
,
Lrnr_lstm
, Lrnr_mean
,
Lrnr_nnls
, Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_randomForest
,
Lrnr_ranger
, Lrnr_rpart
,
Lrnr_sl
, Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_subset_covariates
,
Lrnr_svm
, Lrnr_tsDyn
,
Lrnr_xgboost
, Pipeline
,
Stack
, define_h2o_X
,
undocumented_learner