\name{ses}
\alias{ses}
\alias{holt}
\alias{hw}
\title{Exponential smoothing forecasts}
\usage{ses(x, h=10, level=c(80,95), fan=FALSE, 
    initial=c("optimal","simple"), alpha=NULL, ...)
holt(x, h=10, damped=FALSE, level=c(80,95), fan=FALSE, 
    initial=c("optimal","simple"), exponential=FALSE,
    alpha=NULL, beta=NULL, ...)
hw(x, h=2*frequency(x), seasonal="additive", damped=FALSE, 
    level=c(80,95), fan=FALSE, initial=c("optimal","simple"), 
    exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL, ...)
}

\arguments{
\item{x}{a numeric vector or time series}
\item{h}{Number of periods for forecasting.}
\item{damped}{If TRUE, use a damped trend.}
\item{seasonal}{Type of seasonality in \code{hw} model. "additive" or "multiplicative"}
\item{level}{Confidence level for prediction intervals.}
\item{fan}{If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.}
\item{initial}{Method used for selecting initial state values. If \code{optimal}, the initial values are optimized along with the smoothing parameters using \code{\link{ets}}. If \code{simple}, the initial values are set to values obtained using simple calculations on the first few observations. See Hyndman & Athanasopoulos (2012) for details.}
\item{exponential}{If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear.}
\item{alpha}{Value of smoothing parameter for the level. If \code{NULL}, it will be estimated.}
\item{beta}{Value of smoothing parameter for the trend. If \code{NULL}, it will be estimated.}
\item{gamma}{Value of smoothing parameter for the seasonal component. If \code{NULL}, it will be estimated.}
\item{...}{Other arguments passed to \code{forecast.ets}.}
}

\description{Returns forecasts and other information for exponential smoothing forecasts applied to x.
}
\details{ses, holt and hw are simply convenient wrapper functions for \code{forecast(ets(...))}.
}


\value{An object of class "\code{forecast}".

The function \code{summary} is used to obtain and print a summary of the
results, while the function \code{plot} produces a plot of the forecasts and prediction intervals.

The generic accessor functions \code{fitted.values} and \code{residuals} extract useful features of
the value returned by \code{ets} and associated functions.

An object of class \code{"forecast"} is a list containing at least the following elements:
\item{model}{A list containing information about the fitted model}
\item{method}{The name of the forecasting method as a character string}
\item{mean}{Point forecasts as a time series}
\item{lower}{Lower limits for prediction intervals}
\item{upper}{Upper limits for prediction intervals}
\item{level}{The confidence values associated with the prediction intervals}
\item{x}{The original time series (either \code{object} itself or the time series used to create the model stored as \code{object}).}
\item{residuals}{Residuals from the fitted model. That is x minus fitted values.}
\item{fitted}{Fitted values (one-step forecasts)}
}


\references{Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008) \emph{Forecasting with exponential smoothing: the state space approach}, Springer-Verlag: New York. \url{http://www.exponentialsmoothing.net}.

Hyndman, R.J., Athanasopoulos (2012) \emph{Forecasting: principles and practice}, OTexts: Melbourne, Australia. \url{http://otexts.com/fpp}.
}



\seealso{\code{\link{ets}}, \code{\link[stats]{HoltWinters}}, \code{\link{rwf}}, \code{\link[stats]{arima}}.}

\author{Rob J Hyndman}
\examples{
fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
}
\keyword{ts}