adam_params {modeltime} | R Documentation |
Tuning Parameters for ADAM Models
Description
Tuning Parameters for ADAM Models
Usage
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:
-
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
Examples
use_constant()
regressors_treatment()
distribution()