forecast (version 5.3)

forecast.ets: Forecasting using ETS models

Description

Returns forecasts and other information for univariate ETS models.

Usage

## S3 method for class '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, ...)

Arguments

object
An object of class "ets". Usually the result of a call to ets.
h
Number of periods for forecasting
level
Confidence level for prediction intervals.
fan
If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
simulate
If TRUE, prediction intervals produced by simulation rather than using analytic formulae.
bootstrap
If TRUE, and if simulate=TRUE, then simulation uses resampled errors rather than normally distributed errors.
npaths
Number of sample paths used in computing simulated prediction intervals.
PI
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.
lambda
Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
...
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:

  • modelA list containing information about the fitted model
  • methodThe name of the forecasting method as a character string
  • meanPoint forecasts as a time series
  • lowerLower limits for prediction intervals
  • upperUpper limits for prediction intervals
  • levelThe confidence values associated with the prediction intervals
  • xThe original time series (either object itself or the time series used to create the model stored as object).
  • residualsResiduals 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.
  • fittedFitted values (one-step forecasts)

See Also

ets, ses, holt, hw.

Examples

Run this code
fit <- ets(USAccDeaths)
plot(forecast(fit,h=48))

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