# ets

##### Exponential smoothing state space model

Returns ets model applied to `y`

.

- Keywords
- ts

##### Usage

```
ets(y, model="ZZZ", damped=NULL, alpha=NULL, beta=NULL, gamma=NULL, phi=NULL,
additive.only=FALSE, lower=c(rep(0.0001,3), 0.8), upper=c(rep(0.9999,3),0.98),
opt.crit=c("lik","amse","mse","sigma"), nmse=3,
bounds=c("both","usual","admissible"), ic=c("aic","aicc","bic"),
restrict=TRUE)
```

##### Arguments

- y
- a numeric vector or time series
- model
- Usually a three-character string identifying method using the framework terminology of Hyndman et al. (2002) and Hyndman et al. (2008). The first letter denotes the error type ("A", "M" or "Z"); the second letter denotes the trend type ("N","A","M" or
- damped
- If TRUE, use a damped trend (either additive or multiplicative). If NULL,
both damped and non-damped trends will be tried and the best model (according to the
information criterion
`ic`

) returned. - alpha
- Value of alpha. If NULL, it is estimated.
- beta
- Value of beta. If NULL, it is estimated.
- gamma
- Value of gamma. If NULL, it is estimated.
- phi
- Value of phi. If NULL, it is estimated.
- additive.only
- If TRUE, will only consider additive models. Default is FALSE.
- lower
- Lower bounds for the parameters (alpha, beta, gamma, phi)
- upper
- Upper bounds for the parameters (alpha, beta, gamma, phi)
- opt.crit
- Optimization criterion. One of "mse" (Mean Square Error),
"amse" (Average MSE over first
`nmse`

forecast horizons), "sigma" (Standard deviation of residuals), or "lik" (Log-likelihood, the default). - nmse
- Number of steps for average multistep MSE (1<=
`nmse`

- bounds
- Type of parameter space to impose:
`"usual"`

indicates all parameters must lie between specified lower and upper bounds;`"admissible"`

indicates parameters must lie in the admissible space;`"both"`

(default) takes the - ic
- Information criterion to be used in model selection.
- restrict
- If TRUE, the models with infinite variance will not be allowed.

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##### Details

Based on the classification of methods as described in Hyndman et al (2008).
The methodology is fully automatic. The only required argument for ets is the time series. The model
is chosen automatically if not specified. This methodology performed extremely well on the
M3-competition data. (See Hyndman, et al, 2002, below.)

##### Value

- An object of class "
`ets`

".
The generic accessor functions `fitted.values`

and `residuals`

extract useful features of
the value returned by `ets`

and associated functions.

##### References

Hyndman, R.J., Koehler, A.B., Snyder,
R.D., and Grose, S. (2002) "A state space framework for automatic
forecasting using exponential smoothing methods",
*International J. Forecasting*, **18**(3), 439--454.
Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The
admissible parameter space for exponential smoothing models".
*Annals of Statistical Mathematics*, **60**(2),
407--426.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008)
*Forecasting with exponential smoothing: the state space approach*,
Springer-Verlag. http://robjhyndman.com/expsmooth .

##### See Also

`HoltWinters`

, `rwf`

, `arima`

.

##### Examples

```
fit <- ets(USAccDeaths)
plot(forecast(fit))
```

* Documentation reproduced from package forecast, version 2.13,
License: GPL (>= 2)
*
### Community examples

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