\HeaderA{HoltWinters}{Holt-Winters Filtering}{HoltWinters}
\aliasA{print.HoltWinters}{HoltWinters}{print.HoltWinters}
\aliasA{residuals.HoltWinters}{HoltWinters}{residuals.HoltWinters}
\keyword{ts}{HoltWinters}
\begin{Description}\relax
Computes Holt-Winters Filtering of a given time series.
Unknown parameters are determined by minimizing the squared
prediction error.
\end{Description}
\begin{Usage}
\begin{verbatim}
HoltWinters(x, alpha = NULL, beta = NULL, gamma = NULL,
            seasonal = c("additive", "multiplicative"),
            start.periods = 2, l.start = NULL, b.start = NULL,
            s.start = NULL,
            optim.start = c(alpha = 0.3, beta = 0.1, gamma = 0.1),
            optim.control = list())
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] An object of class \code{ts}
\item[\code{alpha}] \eqn{alpha}{} parameter of Holt-Winters Filter.
\item[\code{beta}] \eqn{beta}{} parameter of Holt-Winters Filter. If set to 0,
the function will do exponential smoothing.
\item[\code{gamma}] \eqn{gamma}{} parameter used for the seasonal component.
If set to 0, an non-seasonal model is fitted.
\item[\code{seasonal}] Character string to select an \code{"additive"}
(the default) or \code{"multiplicative"} seasonal model. The first
few characters are sufficient. (Only takes effect if
\code{gamma} is non-zero).
\item[\code{start.periods}] Start periods used in the autodetection of start
values. Must be at least 2.
\item[\code{l.start}] Start value for level (a[0]).
\item[\code{b.start}] Start value for trend (b[0]).
\item[\code{s.start}] Vector of start values for the seasonal component
(\eqn{s_1[0]...s_p[0]}{s_1[0]...s_p[0]})
\item[\code{optim.start}] Vector with named components \code{alpha},
\code{beta}, and \code{gamma} containing the starting values for the
optimizer. Only the values needed must be specified.  Ignored in the
one-parameter case.
\item[\code{optim.control}] Optional list with additional control parameters
passed to \code{optim} if this is used.  Ignored in the
one-parameter case.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The additive Holt-Winters prediction function (for time series with
period length p) is
\deqn{\hat Y[t+h] = a[t] + h b[t] + s[t + 1 + (h - 1) \bmod p],}{Yhat[t+h] = a[t] + h * b[t] + s[t + 1 + (h - 1) mod p],}
where \eqn{a[t]}{}, \eqn{b[t]}{} and \eqn{s[t]}{} are given by
\deqn{a[t] = \alpha (Y[t] - s[t-p])  + (1-\alpha) (a[t-1] + b[t-1])}{a[t] = \alpha (Y[t] - s[t-p])  + (1-\alpha) (a[t-1] + b[t-1])}
\deqn{b[t] = \beta (a[t] -a[t-1]) + (1-\beta)  b[t-1]}{b[t] = \beta (a[t] - a[t-1]) + (1-\beta) b[t-1]}
\deqn{s[t] = \gamma (Y[t] - a[t])   + (1-\gamma) s[t-p]}{s[t] = \gamma (Y[t] - a[t]) + (1-\gamma) s[t-p]}

The multiplicative Holt-Winters prediction function (for time series
with period length p) is
\deqn{\hat Y[t+h] = (a[t] + h b[t]) \times s[t + 1 + (h - 1) \bmod p].}{Yhat[t+h] = (a[t] + h * b[t]) * s[t + 1 + (h - 1) mod p],}
where \eqn{a[t]}{}, \eqn{b[t]}{} and \eqn{s[t]}{} are given by
\deqn{a[t] = \alpha (Y[t] / s[t-p])  + (1-\alpha) (a[t-1] + b[t-1])}{a[t] = \alpha (Y[t] / s[t-p])  + (1-\alpha) (a[t-1] + b[t-1])}
\deqn{b[t] = \beta (a[t] - a[t-1]) + (1-\beta) b[t-1]}{b[t] = \beta (a[t] - a[t-1]) + (1-\beta) b[t-1]}
\deqn{s[t] = \gamma (Y[t] / a[t])   + (1-\gamma) s[t-p]}{s[t] = \gamma  (Y[t] / a[t])  + (1-\gamma) s[t-p]}

The function tries to find the optimal values of \eqn{\alpha}{} and/or
\eqn{\beta}{} and/or \eqn{\gamma}{} by minimizing the squared
one-step prediction error if they are omitted. \code{optimize} will be
used for the univariate case, and \code{optim} else.

For seasonal models, start values for \code{a}, \code{b} and \code{s}
are detected by
performing a simple decomposition in trend and seasonal component using
moving averages (see function \code{decompose}) on the
\code{start.periods} first periods (a simple linear regression on the
trend component is used for starting level and trend.). For
level/trend-models (no seasonal component), start values for a and b
are x[2] and x[2] - x[1], respectively. For level-only models
(ordinary exponential smoothing), the start value for a is x[1].
\end{Details}
\begin{Value}
An object of class \code{"HoltWinters"}, a list with components:
\begin{ldescription}
\item[\code{fitted}] A multiple time series with one column for the
filtered series as well as for the level, trend and seasonal
components, estimated contemporaneously (that is at time t and not
at the end of the series).
\item[\code{x}] The original series
\item[\code{alpha}] alpha used for filtering
\item[\code{beta}] beta used for filtering
\item[\code{gamma}] gamma used for filtering
\item[\code{coefficients}] A vector with named components \code{a, b, s1, ..., sp}
containing the estimated values for the level, trend and seasonal
components
\item[\code{seasonal}] The specified \code{seasonal}-parameter
\item[\code{SSE}] The final sum of squared errors achieved in optimizing
\item[\code{call}] The call used
\end{ldescription}
\end{Value}
\begin{Author}\relax
David Meyer \email{David.Meyer@wu-wien.ac.at}
\end{Author}
\begin{References}\relax
C. C. Holt (1957)
Forecasting seasonals and trends by exponentially weighted
moving averages,
\emph{ONR Research Memorandum, Carnigie Institute} \bold{52}.

P. R. Winters (1960)
Forecasting sales by exponentially weighted moving averages,
\emph{Management Science} \bold{6}, 324--342.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{predict.HoltWinters}{predict.HoltWinters}},\code{\LinkA{optim}{optim}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
require(graphics)

## Seasonal Holt-Winters
(m <- HoltWinters(co2))
plot(m)
plot(fitted(m))

(m <- HoltWinters(AirPassengers, seasonal = "mult"))
plot(m)

## Non-Seasonal Holt-Winters
x <- uspop + rnorm(uspop, sd = 5)
m <- HoltWinters(x, gamma = 0)
plot(m)

## Exponential Smoothing
m2 <- HoltWinters(x, gamma = 0, beta = 0)
lines(fitted(m2)[,1], col = 3)
\end{ExampleCode}
\end{Examples}

