fable (version 0.0.0.9000)

ETS2: Exponential smoothing state space model

Description

Returns ets model applied to y.

Usage

ETS2(data, formula, restrict = TRUE, ...)

Arguments

data

A data frame

formula

Model specification.

restrict

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

...

Other undocumented arguments.

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.

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.)

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://www.exponentialsmoothing.net.

See Also

HoltWinters, rwf, Arima.

Examples

# NOT RUN {
USAccDeaths %>% ETS2(log(value) ~ season("A"))
# }