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 are produced by simulation rather than using analytic formulae. Errors are assumed to be normally distributed.
`bootstrap` | If TRUE, then prediction intervals are produced by simulation using 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. If `lambda="auto"`, then a transformation is automatically selected using `BoxCox.lambda`. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.
`biasadj` | Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.
`...` | 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 8.4 Index]