R: Forecasting using neural network models

forecast.nnetar {forecast} | R Documentation

## Forecasting using neural network models

### Description

Returns forecasts and other information for univariate neural network models.

### Usage

    ## S3 method for class '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`

### 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.

### 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:

`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
`lower` | Lower limits for prediction intervals
`upper` | Upper limits for prediction intervals
`level` | The confidence values associated with the prediction intervals
`x` | The original time series (either `object` itself or the time series used to create the model stored as `object`).
`xreg` | The external regressors used in fitting (if given).
`residuals` | Residuals from the fitted model. That is x minus fitted values.
`fitted` | Fitted values (one-step forecasts)
`...` | Other arguments

### Author(s)

Rob J Hyndman

### See Also

`nnetar`.

### Examples

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

[Package forecast version 8.2 Index]