Returns ETS model specified by the formula.

```
ETS(formula, opt_crit = c("lik", "amse", "mse", "sigma", "mae"),
nmse = 3, bounds = c("both", "usual", "admissible"), ic = c("aicc",
"aic", "bic"), restrict = TRUE, ...)
```

formula

Model specification.

opt_crit

The optimization criterion. Defaults to the log-likelihood
`"lik"`

, but can also be set to `"mse"`

(Mean Square Error), `"amse"`

(Average MSE over first `nmse`

forecast horizons), `"sigma"`

(Standard
deviation of residuals), or `"mae"`

(Mean Absolute Error).

nmse

If `opt_crit == "amse"`

, `nmse`

provides the number of steps for
average multistep MSE (`1<=nmse<=30`

).

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 intersection of these regions.

ic

The information criterion used in selecting the model.

restrict

If TRUE (default), the models with infinite variance will not be allowed.

...

Other arguments

A `mable`

containing fitted ETS models.

The generic accessor functions `fitted`

and `residuals`

extract useful features of the value returned by `ETS`

and associated
functions.

Based on the classification of methods as described in Hyndman et al (2008).

The methodology is fully automatic. The model is chosen automatically if not specified. This methodology performed extremely well on the M3-competition data. (See Hyndman, et al, 2002, below.)

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://www.exponentialsmoothing.net.

`HoltWinters`

, `RW`

,
`ARIMA`

.

# NOT RUN { USAccDeaths %>% as_tsibble %>% model(ETS(log(value) ~ season("A"))) # }