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.

non_seasonal_ar(), seasonal_ar(), dials::hidden_units(), dials::penalty(), dials::epochs()