This learner supports autoregressive integrated moving average model for univariate time-series.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
order: An optional specification of the non-seasonal
part of the ARIMA model; the three integer components (p, d, q) are the
AR order, the degree of differencing, and the MA order. If order is
specified, then arima will be called; otherwise,
auto.arima will be used to fit the "best" ARIMA
model according to AIC (default), AIC or BIC. The information criterion
to be used in auto.arima model selection can be
modified by specifying ic argument.
num_screen = 5: The top n number of "most impotant" variables to
retain.
...: Other parameters passed to arima or
auto.arima function, depending on whether or not
order argument is provided.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
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_glmnet,
Lrnr_glm,
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_rugarch,
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)
data(bsds)
folds <- make_folds(bsds,
fold_fun = folds_rolling_window, window_size = 500,
validation_size = 100, gap = 0, batch = 50
)
task <- sl3_Task$new(
data = bsds,
folds = folds,
covariates = c(
"weekday", "temp"
),
outcome = "cnt"
)
arima_lrnr <- make_learner(Lrnr_arima)
train_task <- training(task, fold = task$folds[[1]])
valid_task <- validation(task, fold = task$folds[[1]])
arima_fit <- arima_lrnr$train(train_task)
arima_preds <- arima_fit$predict(valid_task)