Returns forecasts and other information for univariate ETS models.

# S3 method for ets
forecast(object, h = ifelse(object$m > 1, 2 * object$m, 10),
level = c(80, 95), fan = FALSE, simulate = FALSE, bootstrap = FALSE,
npaths = 5000, PI = TRUE, lambda = object\$lambda, biasadj = NULL, ...)

## Arguments

object An object of class "ets". Usually the result of a call to ets. Number of periods for forecasting Confidence level for prediction intervals. If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots. If TRUE, prediction intervals are produced by simulation rather than using analytic formulae. Errors are assumed to be normally distributed. If TRUE, then prediction intervals are produced by simulation using resampled errors (rather than normally distributed errors). Number of sample paths used in computing simulated prediction intervals. If TRUE, prediction intervals are produced, otherwise only point forecasts are calculated. If PI is FALSE, then level, fan, simulate, bootstrap and npaths are all ignored. Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation. 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. By default, the value is taken from what was used when fitting the model. 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.ets.

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. For models with additive errors, the residuals are x - fitted values. For models with multiplicative errors, the residuals are equal to x /(fitted values) - 1.

fitted

Fitted values (one-step forecasts)

ets, ses, holt, hw.
fit <- ets(USAccDeaths)