1% Generated by roxygen2: do not edit by hand
2% Please edit documentation in R/ets.R
3\name{ets}
4\alias{ets}
5\alias{print.ets}
6\alias{summary.ets}
7\alias{as.character.ets}
8\alias{coef.ets}
9\alias{tsdiag.ets}
10\title{Exponential smoothing state space model}
11\usage{
12ets(
13  y,
14  model = "ZZZ",
15  damped = NULL,
16  alpha = NULL,
17  beta = NULL,
18  gamma = NULL,
19  phi = NULL,
20  additive.only = FALSE,
21  lambda = NULL,
22  biasadj = FALSE,
23  lower = c(rep(1e-04, 3), 0.8),
24  upper = c(rep(0.9999, 3), 0.98),
25  opt.crit = c("lik", "amse", "mse", "sigma", "mae"),
26  nmse = 3,
27  bounds = c("both", "usual", "admissible"),
28  ic = c("aicc", "aic", "bic"),
29  restrict = TRUE,
30  allow.multiplicative.trend = FALSE,
31  use.initial.values = FALSE,
32  na.action = c("na.contiguous", "na.interp", "na.fail"),
33  ...
34)
35}
36\arguments{
37\item{y}{a numeric vector or time series of class \code{ts}}
38
39\item{model}{Usually a three-character string identifying method using the
40framework terminology of Hyndman et al. (2002) and Hyndman et al. (2008).
41The first letter denotes the error type ("A", "M" or "Z"); the second letter
42denotes the trend type ("N","A","M" or "Z"); and the third letter denotes
43the season type ("N","A","M" or "Z"). In all cases, "N"=none, "A"=additive,
44"M"=multiplicative and "Z"=automatically selected. So, for example, "ANN" is
45simple exponential smoothing with additive errors, "MAM" is multiplicative
46Holt-Winters' method with multiplicative errors, and so on.
47
48It is also possible for the model to be of class \code{"ets"}, and equal to
49the output from a previous call to \code{ets}. In this case, the same model
50is fitted to \code{y} without re-estimating any smoothing parameters. See
51also the \code{use.initial.values} argument.}
52
53\item{damped}{If TRUE, use a damped trend (either additive or
54multiplicative). If NULL, both damped and non-damped trends will be tried
55and the best model (according to the information criterion \code{ic})
56returned.}
57
58\item{alpha}{Value of alpha. If NULL, it is estimated.}
59
60\item{beta}{Value of beta. If NULL, it is estimated.}
61
62\item{gamma}{Value of gamma. If NULL, it is estimated.}
63
64\item{phi}{Value of phi. If NULL, it is estimated.}
65
66\item{additive.only}{If TRUE, will only consider additive models. Default is
67FALSE.}
68
69\item{lambda}{Box-Cox transformation parameter. If \code{lambda="auto"},
70then a transformation is automatically selected using \code{BoxCox.lambda}.
71The transformation is ignored if NULL. Otherwise,
72data transformed before model is estimated. When \code{lambda} is specified,
73\code{additive.only} is set to \code{TRUE}.}
74
75\item{biasadj}{Use adjusted back-transformed mean for Box-Cox
76transformations. If transformed data is used to produce forecasts and fitted values,
77a regular back transformation will result in median forecasts. If biasadj is TRUE,
78an adjustment will be made to produce mean forecasts and fitted values.}
79
80\item{lower}{Lower bounds for the parameters (alpha, beta, gamma, phi)}
81
82\item{upper}{Upper bounds for the parameters (alpha, beta, gamma, phi)}
83
84\item{opt.crit}{Optimization criterion. One of "mse" (Mean Square Error),
85"amse" (Average MSE over first \code{nmse} forecast horizons), "sigma"
86(Standard deviation of residuals), "mae" (Mean of absolute residuals), or
87"lik" (Log-likelihood, the default).}
88
89\item{nmse}{Number of steps for average multistep MSE (1<=\code{nmse}<=30).}
90
91\item{bounds}{Type of parameter space to impose: \code{"usual" } indicates
92all parameters must lie between specified lower and upper bounds;
93\code{"admissible"} indicates parameters must lie in the admissible space;
94\code{"both"} (default) takes the intersection of these regions.}
95
96\item{ic}{Information criterion to be used in model selection.}
97
98\item{restrict}{If \code{TRUE} (default), the models with infinite variance
99will not be allowed.}
100
101\item{allow.multiplicative.trend}{If \code{TRUE}, models with multiplicative
102trend are allowed when searching for a model. Otherwise, the model space
103excludes them. This argument is ignored if a multiplicative trend model is
104explicitly requested (e.g., using \code{model="MMN"}).}
105
106\item{use.initial.values}{If \code{TRUE} and \code{model} is of class
107\code{"ets"}, then the initial values in the model are also not
108re-estimated.}
109
110\item{na.action}{A function which indicates what should happen when the data
111contains NA values. By default, the largest contiguous portion of the
112time-series will be used.}
113
114\item{...}{Other undocumented arguments.}
115}
116\value{
117An object of class "\code{ets}".
118
119The generic accessor functions \code{fitted.values} and \code{residuals}
120extract useful features of the value returned by \code{ets} and associated
121functions.
122}
123\description{
124Returns ets model applied to \code{y}.
125}
126\details{
127Based on the classification of methods as described in Hyndman et al (2008).
128
129The methodology is fully automatic. The only required argument for ets is
130the time series. The model is chosen automatically if not specified. This
131methodology performed extremely well on the M3-competition data. (See
132Hyndman, et al, 2002, below.)
133}
134\examples{
135fit <- ets(USAccDeaths)
136plot(forecast(fit))
137
138}
139\references{
140Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002)
141"A state space framework for automatic forecasting using exponential
142smoothing methods", \emph{International J. Forecasting}, \bold{18}(3),
143439--454.
144
145Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible
146parameter space for exponential smoothing models". \emph{Annals of
147Statistical Mathematics}, \bold{60}(2), 407--426.
148
149Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008)
150\emph{Forecasting with exponential smoothing: the state space approach},
151Springer-Verlag. \url{http://www.exponentialsmoothing.net}.
152}
153\seealso{
154\code{\link[stats]{HoltWinters}}, \code{\link{rwf}},
155\code{\link{Arima}}.
156}
157\author{
158Rob J Hyndman
159}
160\keyword{ts}
161