# ses

##### 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,
initial=c("optimal","simple"), alpha=NULL, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE,
initial=c("optimal","simple"), exponential=FALSE,
alpha=NULL, beta=NULL, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE,
level=c(80,95), fan=FALSE, initial=c("optimal","simple"),
exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL, ...)
```

##### 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.
- initial
- Method used for selecting initial state values. If
`optimal`

, the initial values are optimized along with the smoothing parameters using`ets`

. If`simple`

, the initial values are set to val - exponential
- If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear.
- alpha
- Value of smoothing parameter for the level. If
`NULL`

, it will be estimated. - beta
- Value of smoothing parameter for the trend. If
`NULL`

, it will be estimated. - gamma
- Value of smoothing parameter for the seasonal component. If
`NULL`

, it will be estimated. - ...
- Other arguments passed to
`forecast.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., Ord, J.K., Snyder, R.D. (2008) *Forecasting with exponential smoothing: the state space approach*, Springer-Verlag: New York.

Hyndman, R.J., Athanasopoulos (2012) *Forecasting: principles and practice*, OTexts: Melbourne, Australia.

##### 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 4.04, License: GPL (>= 2)*