This learner supports exponential smoothing models using the forecast
package. Fitting is done with the ets
function.
Lrnr_expSmooth
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
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.
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