R: Forecasting using ETS models

forecast.ets {forecast} | R Documentation

## 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, 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))

[Package forecast version 8.0 Index]