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Tuning Parameters for NNETAR Models


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



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.


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.


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.


#> Number of Neural Networks to Average (quantitative)
#> Range: [1, 100]