# adam_params: Tuning Parameters for ADAM Models In modeltime: The Tidymodels Extension for Time Series Modeling

## Tuning Parameters for ADAM Models

### Usage

``````use_constant(values = c(FALSE, TRUE))

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()

``````

modeltime documentation built on Sept. 2, 2023, 5:06 p.m.