forecast.ets: Forecasting using ETS models

In pli2016/forecast: Forecasting Functions for Time Series and Linear 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, biasadj=NULL, ...)

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(51,99,by=3). 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.
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. 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)

Author(s)

Rob J Hyndman

See Also

ets, ses, holt, hw.

Examples

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

pli2016/forecast documentation built on May 25, 2019, 8:22 a.m.