This learner supports exponential smoothing models using the forecast package. Fitting is done with the ets function.

Lrnr_expSmooth

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

model="ZZZ"

Three-character string identifying method. In all cases, "N"=none, "A"=additive, "M"=multiplicative, and "Z"=automatically selected. The first letter denotes the error type, second letter denotes the trend type, third letter denotes the season type. For example, "ANN" is simple exponential smoothing with additive errors, "MAM" is multiplicative Holt-Winters' methods with multiplicative errors, etc.

damped=NULL

If TRUE, use a damped trend (either additive or multiplicative). If NULL, both damped and non-damped trends will be tried and the best model (according to the information criterion ic) returned.

alpha=NULL

Value of alpha. If NULL, it is estimated.

beta=NULL

Value of beta. If NULL, it is estimated.

gamma=NULL

Value of gamma. If NULL, it is estimated.

phi=NULL

Value of phi. If NULL, it is estimated.

lambda=NULL

Box-Cox transformation parameter. Ignored if NULL. When lambda is specified, additive.only is set to TRUE.

additive.only=FALSE

If TRUE, will only consider additive models.

biasadj=FALSE

Use adjusted back-transformed mean for Box-Cox transformations.

lower=c(rep(1e-04, 3), 0.8)

Lower bounds for the parameters (alpha, beta, gamma, phi).

upper=c(rep(0.9999,3), 0.98)

Upper bounds for the parameters (alpha, beta, gamma, phi)

opt.crit="lik"

Optimization criterion.

nmse=3

Number of steps for average multistep MSE (1 <= nmse <= 30).

bounds="both"

Type of parameter space to impose: "usual" indicates all parameters must lie between specified lower and upper bounds; "admissible" indicates parameters must lie in the admissible space; "both" (default) takes the intersection of these regions.

ic="aic"

Information criterion to be used in model selection.

restrict=TRUE

If TRUE, models with infinite variance will not be allowed.

allow.multiplicative.trend=FALSE

If TRUE, models with multiplicative trend are allowed when searching for a model.

use.initial.values=FALSE

If TRUE and model is of class "ets", then the initial values in the model are also not re-estimated.

n.ahead

The forecast horizon. If not specified, returns forecast of size task$X.

freq=1

the number of observations per unit of time.

See also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bilstm, Lrnr_condensier, Lrnr_cv, Lrnr_define_interactions, 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_rugarch, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_xgboost, Pipeline, Stack, define_h2o_X, undocumented_learner