Forecast method for hybrid models.
# S3 method for hybridModel
forecast(
object,
h = ifelse(object$frequency > 1, 2 * object$frequency, 10),
xreg = NULL,
level = c(80, 95),
PI = TRUE,
fan = FALSE,
PI.combination = c("extreme", "mean"),
...
)An object of class forecast.
a hybrid time series model fit with hybridModel.
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.
future values of regression variables (for use if one of the ensemble methods used
in creating the hybrid forecast was auto.arima, nnetar, or stlm
and the model(s) used xreg in the fit).
It should be supplied as a matrix.
confidence level for prediction intervals. This can be expressed as a decimal between 0.0 and 1.0 or numeric between 0 and 100.
should prediction intervals be produced? If a nnetar model is in the ensemble,
this can be quite slow, so disabling prediction intervals will speed up the forecast generation.
If FALSE, the arguments level and fan are ignored.
if TRUE, level is set to seq(51, 99, by = 3). This is suitable
for fan plots.
Method for combining the prediction intervals from each of the
forecasts. Supplying "mean"
will simply average each of the lower/upper intervals from each model without using the model
weights used for the point forecasts. The default value "extreme" will take
the most pessimistic intervals (i.e. the highest upper interval from all the component models
and the lowest prediction interval from all of the component models').
other arguments passed to the individual forecast generic methods.
David Shaub
if xreg was used in constructing the hybridModel,
it must also be passed into forecast.hybridModel.
While prediction intervals are produced for the
final ensemble forecast model, these should be viewed conservatively as insights
to the forecast's uncertainty. Currently these are constructed using the most extreme interval
from each component model for each horizon, so the composite prediction intervals do not
have statistical guarantees of asymptotic efficiency. More sophisticated
and rigorous techniques are planned, however, particularly when cross validation
approaches are used.
hybridModel
if (FALSE) {
mod <- hybridModel(AirPassengers)
fc <- forecast(mod)
# View the point forecasts
fc$mean
# View the upper prediction interval
fc$upper
# View the lower prediction interval
fc$lower
# Plot the forecast
plot(fc)
}
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