Returns forecasts and other information for univariate structural time series models.

# S3 method for StructTS
forecast(object, h = ifelse(object$coef["epsilon"] > 1e-10,
  2 * object$xtsp[3], 10), level = c(80, 95), fan = FALSE, lambda = NULL,
  biasadj = NULL, ...)

Arguments

object

An object of class "StructTS". Usually the result of a call to StructTS.

h

Number of periods for forecasting

level

Confidence level for prediction intervals.

fan

If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.

lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.

biasadj

Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.

...

Other arguments.

Value

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.

The generic accessor functions fitted.values and residuals extract useful features of the value returned by forecast.StructTS.

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 object itself or the time series used to create the model stored as object).

residuals

Residuals from the fitted model. That is x minus fitted values.

fitted

Fitted values (one-step forecasts)

Details

This function calls predict.StructTS and constructs an object of class "forecast" from the results.

See also

StructTS.

Examples

fit <- StructTS(WWWusage,"level") plot(forecast(fit))