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

.

```
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,
...
)

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.

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)

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

.

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/

`ets`

, `HoltWinters`

,
`rwf`

, `arima`

.

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

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