R: Exponential smoothing state space model

ets {forecast}

R Documentation

Exponential smoothing state space model

Description

Returns ets model applied to y.

Usage

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)

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 "Z"); and the third letter denotes the season type ("N","A","M" or "Z"). In all cases, "N"=none, "A"=additive, "M"=multiplicative and "Z"=automatically selected. So, for example, "ANN" is simple exponential smoothing with additive errors, "MAM" is multiplicative Holt-Winters' method with multiplicative errors, and so on. It is also possible for the model to be equal to the output from a previous call to `ets`. In this case, the same model is fitted to `y` without re-estimating any parameters.
`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.
`lambda`	Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated. When `lambda=TRUE`, `additive.only` is set to 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), "mae" (Mean of absolute residuals), or "lik" (Log-likelihood, the default).
`nmse`	Number of steps for average multistep MSE (1<=`nmse`<=10).
`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`	Information criterion to be used in model selection.
`restrict`	If TRUE, the models with infinite variance will not be allowed.

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.

Author(s)

Rob J Hyndman

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.

Examples

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

[Package forecast version 3.24 Index]