This learner supports autoregressive fractionally integrated
moving average and various flavors of generalized autoregressive
conditional heteroskedasticity models for univariate time-series. All the
models are fit using ugarchfit.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
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.
distribution.model: Conditional density to be used for the
innovations.
start.pars:List of staring parameters for the optimization
routine.
fixed.pars:List of parameters which are to be kept fixed during
the optimization routine.
...: Other parameters passed to ugarchfit.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_bartMachine,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv_selector,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_earth,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_stratified,
Lrnr_subset_covariates,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
library(origami)
library(data.table)
data(bsds)
# make folds appropriate for time-series cross-validation
folds <- make_folds(bsds,
fold_fun = folds_rolling_window, window_size = 500,
validation_size = 100, gap = 0, batch = 50
)
# build task by passing in external folds structure
task <- sl3_Task$new(
data = bsds,
folds = folds,
covariates = c(
"weekday", "temp"
),
outcome = "cnt"
)
# create tasks for taining and validation
train_task <- training(task, fold = task$folds[[1]])
valid_task <- validation(task, fold = task$folds[[1]])
# instantiate learner, then fit and predict
HarReg_learner <- Lrnr_HarmonicReg$new(K = 7, freq = 105)
HarReg_fit <- HarReg_learner$train(train_task)
HarReg_preds <- HarReg_fit$predict(valid_task)