adam_params {modeltime} | R Documentation |
Tuning Parameters for ADAM Models
Description
Tuning Parameters for ADAM Models
Usage
ets_model(values = c("ZZZ", "XXX", "YYY", "CCC", "PPP", "FFF"))
loss(
values = c("likelihood", "MSE", "MAE", "HAM", "LASSO", "RIDGE", "TMSE", "GTMSE",
"MSEh", "MSCE")
)
use_constant(values = c(FALSE, TRUE))
regressors_treatment(values = c("use", "select", "adapt"))
outliers_treatment(values = c("ignore", "use", "select"))
probability_model(
values = c("none", "auto", "fixed", "general", "odds-ratio", "inverse-odds-ratio",
"direct")
)
distribution(
values = c("default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss",
"dgamma")
)
information_criteria(values = c("AICc", "AIC", "BICc", "BIC"))
select_order(values = c(FALSE, TRUE))
Arguments
values |
A character string of possible values. |
Details
The main parameters for ADAM models are:
-
ets_model
:model="ZZZ" means that the model will be selected based on the chosen information criteria type. The Branch and Bound is used in the process.
model="XXX" means that only additive components are tested, using Branch and Bound.
model="YYY" implies selecting between multiplicative components.
model="CCC" triggers the combination of forecasts of models using information criteria weights (Kolassa, 2011).
combinations between these four and the classical components are also accepted. For example, model="CAY" will combine models with additive trend and either none or multiplicative seasonality.
model="PPP" will produce the selection between pure additive and pure multiplicative models. "P" stands for "Pure". This cannot be mixed with other types of components.
model="FFF" will select between all the 30 types of models. "F" stands for "Full". This cannot be mixed with other types of components.
The parameter model can also be a vector of names of models for a finer tuning (pool of models). For example, model=c("ANN","AAA") will estimate only two models and select the best of them.
-
loss
:likelihood - the model is estimated via the maximization of the likelihood of the function specified in distribution;
MSE (Mean Squared Error),
MAE (Mean Absolute Error),
HAM (Half Absolute Moment),
LASSO - use LASSO to shrink the parameters of the model;
RIDGE - use RIDGE to shrink the parameters of the model;
TMSE - Trace Mean Squared Error,
GTMSE - Geometric Trace Mean Squared Error,
MSEh - optimisation using only h-steps ahead error,
MSCE - Mean Squared Cumulative Error.
-
non_seasonal_ar
: The order of the non-seasonal auto-regressive (AR) terms. -
non_seasonal_differences
: The order of integration for non-seasonal differencing. -
non_seasonal_ma
: The order of the non-seasonal moving average (MA) terms. -
seasonal_ar
: The order of the seasonal auto-regressive (SAR) terms. -
seasonal_differences
: The order of integration for seasonal differencing. -
seasonal_ma
: The order of the seasonal moving average (SMA) terms. -
use_constant
: Logical, determining, whether the constant is needed in the model or not. -
regressors_treatment
: The variable defines what to do with the provided explanatory variables. -
outliers_treatment
: Defines what to do with outliers. -
probability_model
: The type of model used in probability estimation. -
distribution
: What density function to assume for the error term. -
information_criteria
: The information criterion to use in the model selection / combination procedure. -
select_order
: If TRUE, then the function will select the most appropriate order.
Value
A dials
parameter
A parameter
A parameter
A parameter
A parameter
A parameter
A parameter
A parameter
A parameter
A parameter
Examples
use_constant()
regressors_treatment()
distribution()