# ses

From forecast v7.3
by Rob Hyndman

##### Exponential smoothing forecasts

Returns forecasts and other information for exponential smoothing forecasts applied to `y`

.

- Keywords
- ts

##### Usage

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

##### Arguments

- y
- 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(51,99,by=3). 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 values obtained using simple calculations on the first few observations. See Hyndman & Athanasopoulos (2014) for details. - 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. - lambda
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated. When
`lambda=TRUE`

,`additive.only`

is set to FALSE. - 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.
- x
- Deprecated. Included for backwards compatibility.
- ...
- Other arguments passed to
`forecast.ets`

.

##### Details

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

.

##### Value

`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:
is a list containing at least the following elements:##### 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. http://www.exponentialsmoothing.net.

Hyndman, R.J., Athanasopoulos (2014) *Forecasting: principles and practice*, OTexts: Melbourne, Australia. http://www.otexts.org/fpp.

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

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