# nnetar

##### Neural Network Time Series Forecasts

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

- Keywords
- ts

##### Usage

```
nnetar(x, p, P=1, size, repeats=20, lambda=NULL)
## S3 method for class 'nnetar':
forecast(object, h=ifelse(object$m > 1, 2 * object$m, 10),
lambda=object$lambda, ...)
```

##### Arguments

- x
- a numeric vector or time series
- p
- Embedding dimension for non-seasonal time series. Number of non-seasonal lags used as inputs. For non-seasonal time series, the default is the optimal number of lags (according to the AIC) for a linear AR(p) model. For seasonal time series, the same metho
- P
- Number of seasonal lags used as inputs.
- size
- Number of nodes in the hidden layer. Default is half of the number of input nodes plus 1.
- repeats
- Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.
- lambda
- Box-Cox transformation parameter.
- object
- An object of class
`nnetar`

generated by`nnetar`

. - h
- Number of periods for forecasting.
- ...
- Other arguments.

##### Details

A feed-forward neural network is fitted with lagged values of `x`

as inputs and a single hidden layer with `size`

nodes. The inputs are for lags 1 to `p`

, and lags `m`

to `mP`

where `m=frequency(x)`

. A total of `repeats`

networks are fitted, each with random starting weights. These are then averaged when computing forecasts. The network is trained for one-step forecasting. Multi-step forecasts are computed recursively. The fitted model is called an NNAR(p,P) model and is analogous to an ARIMA(p,0,0)(P,0,0) model but with nonlinear functions.

##### Value

`nnetar`

returns an object of class "`nnetar`

".`forecast.nnetar`

returns an object of class "`forecast`

".The function

`summary`

is used to obtain and print a summary of the results, while the function`plot`

produces a plot of the forecasts.The generic accessor functions

`fitted.values`

and`residuals`

extract useful features of the value returned by`nnetar`

.An object of class

`"forecast"`

is a list containing at least the following elements:model A list containing information about the fitted model method The name of the forecasting method as a character string mean Point forecasts as a time series x The original time series (either `object`

itself or the time series used to create the model stored as`object`

).residuals Residuals from the fitted model. That is x minus fitted values. fitted Fitted values (one-step forecasts) ... Other arguments

##### Examples

```
fit <- nnetar(lynx)
fcast <- forecast(fit)
plot(fcast)
```

*Documentation reproduced from package forecast, version 5.3, License: GPL (>= 2)*