forecast.nnetar: Forecasting using neural network models

Description

Returns forecasts and other information for univariate neural network models.

Usage

"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.

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.

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

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: " is a list containing at least the following elements:

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.

Examples

Run this code

fcast <- forecast(fit)
plot(fcast)