Tuning Parameters for NNETAR Models

## Usage

`num_networks(range = c(1L, 100L), trans = NULL)`

## Arguments

- range
A two-element vector holding the

*defaults*for the smallest and largest possible values, respectively. If a transformation is specified, these values should be in the*transformed units*.- trans
A

`trans`

object from the`scales`

package, such as`scales::log10_trans()`

or`scales::reciprocal_trans()`

. If not provided, the default is used which matches the units used in`range`

. If no transformation,`NULL`

.

## Details

The main parameters for NNETAR models are:

`non_seasonal_ar`

: Number of non-seasonal auto-regressive (AR) lags. Often denoted "p" in pdq-notation.`seasonal_ar`

: Number of seasonal auto-regressive (SAR) lags. Often denoted "P" in PDQ-notation.`hidden_units`

: An integer for the number of units in the hidden model.`num_networks`

: Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.`penalty`

: A non-negative numeric value for the amount of weight decay.`epochs`

: An integer for the number of training iterations.