Forecasting using a bagged model
Returns forecasts and other information for bagged models.
## S3 method for class 'baggedModel' forecast( object, h = ifelse(frequency(object$y) > 1, 2 * frequency(object$y), 10), ... )
object |
An object of class " |
h |
Number of periods for forecasting. |
... |
Other arguments, passed on to the |
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.
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.
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 |
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) |
Christoph Bergmeir, Fotios Petropoulos
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.
fit <- baggedModel(WWWusage) fcast <- forecast(fit) plot(fcast) ## Not run: fit2 <- baggedModel(WWWusage, fn="auto.arima") fcast2 <- forecast(fit2) plot(fcast2) accuracy(fcast2) ## End(Not run)
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