fable (version 0.0.0.9100)

ETS: Exponential smoothing state space model

Description

Returns ETS model specified by the formula.

Usage

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

Arguments

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

Value

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.

Details

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.)

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, RW, ARIMA.

Examples

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