Returns forecasts and other information for univariate neural network
models.

# S3 method for nnetar
forecast(object, h = ifelse(object$m > 1, 2 * object$m, 10),
PI = FALSE, level = c(80, 95), fan = FALSE, xreg = NULL,
lambda = object$lambda, bootstrap = FALSE, npaths = 1000,
innov = NULL, ...)

## Arguments

object |
An object of class "`nnetar` " resulting from a call to
`arima` . |

h |
Number of periods for forecasting. If `xreg` is used, `h`
is ignored and the number of forecast periods is set to the number of rows
of `xreg` . |

PI |
If TRUE, prediction intervals are produced, otherwise only point
forecasts are calculated. If `PI` is FALSE, then `level` ,
`fan` , `bootstrap` and `npaths` are all ignored. |

level |
Confidence level for prediction intervals. |

fan |
If `TRUE` , level is set to `seq(51,99,by=3)` . This is
suitable for fan plots. |

xreg |
Future values of external regressor variables. |

lambda |
Box-Cox transformation parameter. Ignored if NULL. Otherwise,
forecasts back-transformed via an inverse Box-Cox transformation. |

bootstrap |
If `TRUE` , then prediction intervals computed using
simulations with resampled residuals rather than normally distributed
errors. Ignored if `innov` is not `NULL` . |

npaths |
Number of sample paths used in computing simulated prediction
intervals. |

innov |
Values to use as innovations for prediction intervals. Must be
a matrix with `h` rows and `npaths` columns (vectors are coerced
into a matrix). If present, `bootstrap` is ignored. |

... |
Additional arguments passed to `simulate.nnetar` |

## Value

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 and
prediction intervals.

The generic accessor functions `fitted.values`

and `residuals`

extract useful features of the value returned by `forecast.nnetar`

.

An object of class "`forecast`

" is a list containing at least the
following elements:

modelA list containing information about the fitted model

methodThe name of the forecasting method as a character string

meanPoint forecasts as a time series

lowerLower limits for prediction intervals

upperUpper limits for prediction intervals

levelThe confidence values associated with the prediction intervals

xThe original time series (either `object`

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

).

xregThe external regressors used in fitting (if given).

residualsResiduals from the fitted model. That is x minus fitted values.

fittedFitted values (one-step forecasts)

...Other arguments

## Details

Prediction intervals are calculated through simulations and can be slow.
Note that if the network is too complex and overfits the data, the residuals
can be arbitrarily small; if used for prediction interval calculations, they
could lead to misleadingly small values.

## See also

`nnetar`

.

## Examples