modeltime (version 1.3.0)

adam_params: 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))

Value

A dials parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

A parameter

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.

Examples

use_constant()

regressors_treatment()

distribution()