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

.

## 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. 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.

- 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.

## 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.

- 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. http://www.exponentialsmoothing.net.

Hyndman and Athanasopoulos (2018) *Forecasting: principles
and practice*, 2nd edition, OTexts: Melbourne, Australia.
https://otexts.com/fpp2/

## See also

`ets`

, `HoltWinters`

,
`rwf`

, `arima`

.