y
.ets(y, model="ZZZ", damped=NULL, alpha=NULL, beta=NULL, gamma=NULL,
phi=NULL, additive.only=FALSE, lambda=NULL,
lower=c(rep(0.0001,3), 0.8), upper=c(rep(0.9999,3),0.98),
opt.crit=c("lik","amse","mse","sigma","mae"), nmse=3,
bounds=c("both","usual","admissible"), ic=c("aic","aicc","bic"),
restrict=TRUE, ...)
ic
) returned.lambda=TRUE
, additive.only
is set to FALSE.nmse
forecast horizons), "sigma"
(Standard deviation of residuals), "mae" (Mean of absolute residuals), or "lik" (Log-likelihood, the default).nmse=
"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 inets
".The generic accessor functions fitted.values
and residuals
extract useful features of
the value returned by ets
and associated functions.
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.)
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.
HoltWinters
, rwf
, arima
.fit <- ets(USAccDeaths)
plot(forecast(fit))
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