# ses

##### 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, phi = 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, phi = NULL, lambda = NULL, biasadj = FALSE, x = y,
...)

##### Arguments

- y
a numeric vector or time series of class

`ts`

- 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.

- 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.- alpha
Value of smoothing parameter for the level. 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`

.- damped
If TRUE, use a damped trend.

- exponential
If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear.

- beta
Value of smoothing parameter for the trend. If

`NULL`

, it will be estimated.- phi
Value of damping parameter if

`damped=TRUE`

. If`NULL`

, it will be estimated.- seasonal
Type of seasonality in

`hw`

model. "additive" or "multiplicative"- gamma
Value of smoothing parameter for the seasonal component. If

`NULL`

, it will be estimated.

##### 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:

A list containing information about the fitted model

The name of the forecasting method as a character string

Point forecasts as a time series

Lower limits for prediction intervals

Upper limits for prediction intervals

The confidence values associated with the prediction intervals

The original time series
(either `object`

itself or the time series used to create the model
stored as `object`

).

Residuals from the fitted model.

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. 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

```
# NOT RUN {
fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
# }
```

*Documentation reproduced from package forecast, version 8.1, License: GPL (>= 3)*