forecast.ets {forecast} | R Documentation |
Returns forecasts and other information for univariate ETS models.
## 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=FALSE, ...)
object |
An object of class " |
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 |
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 |
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. |
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 |
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) |
Rob J Hyndman
fit <- ets(USAccDeaths) plot(forecast(fit,h=48))