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::transform_log10() or scales::transform_reciprocal(). 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.
non_seasonal_ar(), seasonal_ar(), dials::hidden_units(), dials::penalty(), dials::epochs()