Lrnr_arima: Univariate ARIMA Models
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters See Also Examples

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)