# ses

From forecast v2.13
by Rob Hyndman

##### Exponential smoothing forecasts

Returns forecasts and other information for exponential smoothing forecasts applied to x.

- Keywords
- ts

##### Usage

```
ses(x, h=10, level=c(80,95), fan=FALSE, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE, level=c(80,95), fan=FALSE, ...)
```

##### Arguments

- x
- a numeric vector or time series
- h
- Number of periods for forecasting.
- damped
- If TRUE, use a damped trend.
- seasonal
- Type of seasonality in
`hw`

model. "additive" or "multiplicative" - level
- Confidence level for prediction intervals.
- fan
- If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
- ...
- Other arguments passed to
`ets`

.

##### Details

ses, holt and hw are simply convenient wrapper functions for `forecast(ets(...))`

.

##### 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`ets`

and associated functions. 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. That is x minus fitted values. fitted Fitted values (one-step forecasts)

##### References

Hyndman, R.J., Koehler, A.B., Snyder,
R.D., Grose, S. (2002) "A state space framework for automatic
forecasting using exponential smoothing methods",
*International J. Forecasting*, **18**(3), 439--454.
Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The
admissible parameter space for exponential smoothing models".
*Annals of Statistical Mathematics*, **60**(2),
407--426.

##### See Also

`ets`

, `HoltWinters`

, `rwf`

, `arima`

.

##### Examples

```
fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
```

*Documentation reproduced from package forecast, version 2.13, License: GPL (>= 2)*

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