forecast (version 9.0.0)

forecast.baggedModel: Forecasting using a bagged model

Description

Returns forecasts and other information for bagged models.

Usage

# S3 method for baggedModel
forecast(
  object,
  h = if (frequency(object$y) > 1) 2 * frequency(object$y) else 10,
  ...
)

Value

An object of class forecast.

Arguments

object

An object of class baggedModel resulting from a call to baggedModel().

h

Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).

...

Other arguments, passed on to the forecast() function of the original method

forecast class

An object of class forecast is a list usually 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.

residuals

Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.

fitted

Fitted values (one-step forecasts)

The function summary can be used to obtain and print a summary of the results, while the functions plot and autoplot produce plots of the forecasts and prediction intervals. The generic accessors functions fitted.values and residuals extract various useful features from the underlying model.

Author

Christoph Bergmeir, Fotios Petropoulos

Details

Intervals are calculated as min and max values over the point forecasts from the models in the ensemble. I.e., the intervals are not prediction intervals, but give an indication of how different the forecasts within the ensemble are.

References

Bergmeir, C., R. J. Hyndman, and J. M. Benitez (2016). Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation. International Journal of Forecasting 32, 303-312.

See Also

baggedModel().

Examples

fit <- baggedModel(WWWusage)
fcast <- forecast(fit)
plot(fcast)

if (FALSE) {
fit2 <- baggedModel(WWWusage, fn = "auto.arima")
fcast2 <- forecast(fit2)
plot(fcast2)
accuracy(fcast2)
}