Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate binary time series.

## Arguments

- formula
Model specification (see "Specials" section).

- n_nodes
Number of nodes in the hidden layer. Default is half of the number of external regressors plus 1.

- n_networks
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.

- scale_inputs
If TRUE, inputs are scaled by subtracting the column means and dividing by their respective standard deviations.

- ...
Other arguments passed to

`\link[nnet]{nnet}`

.

## Details

A feed-forward neural network is fitted with a single hidden layer containing `size`

nodes.

Exogenous regressors are used as inputs.
A total of `repeats`

networks are
fitted, each with random starting weights. These are then averaged when
computing forecasts.

## Examples

```
melb_rain |>
model(nn = BINNET(Wet ~ fourier(K = 1, period = "year")))
#> # A mable: 1 x 1
#> nn
#> <model>
#> 1 <BINNET: 2>
```