Forecasting using neural network models
Returns forecasts and other information for univariate neural network models.
"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, ...)
- An object of class "
nnetar" resulting from a call to
- Number of periods for forecasting. If
his ignored and the number of forecast periods is set to the number of rows of
- If TRUE, prediction intervals are produced, otherwise only
point forecasts are calculated. If
PIis FALSE, then
npathsare all ignored.
- Confidence level for prediction intervals.
TRUE, level is set to
seq(51,99,by=3). This is suitable for fan plots.
- Future values of external regressor variables.
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
TRUE, then prediction intervals computed using simulations with resampled residuals rather than normally distributed errors. Ignored if
- Number of sample paths used in computing simulated prediction intervals.
- Values to use as innovations for prediction intervals. Must
be a matrix with
npathscolumns (vectors are coerced into a matrix). If present,
- Additional arguments passed to
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.
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts and prediction intervals.The generic accessor functions
residualsextract 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:
fcast <- forecast(fit) plot(fcast)