\HeaderA{arima.sim}{Simulate from an ARIMA Model}{arima.sim}
\keyword{ts}{arima.sim}
\begin{Description}\relax
Simulate from an ARIMA model.
\end{Description}
\begin{Usage}
\begin{verbatim}
arima.sim(model, n, rand.gen = rnorm, innov = rand.gen(n, ...),
          n.start = NA, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{model}] A list with component \code{ar} and/or \code{ma} giving
the AR and MA coefficients respectively.  Optionally a component
\code{order} can be used. An empty list gives an ARIMA(0, 0, 0)
model, that is white noise.
\item[\code{n}] length of output series, before un-differencing.
\item[\code{rand.gen}] optional: a function to generate the innovations.
\item[\code{innov}] an optional times series of innovations.  If not
provided, \code{rand.gen} is used.
\item[\code{n.start}] length of \dQuote{burn-in} period.  If \code{NA}, the
default, a reasonable value is computed.
\item[\code{...}] additional arguments for \code{rand.gen}.  Most usefully,
the standard deviation of the innovations generated by \code{rnorm}
can be specified by \code{sd}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
See \code{\LinkA{arima}{arima}} for the precise definition of an ARIMA model.

The ARMA model is checked for stationarity.

ARIMA models are specified via the \code{order} component of
\code{model}, in the same way as for \code{\LinkA{arima}{arima}}.  Other
aspects of the \code{order} component are ignored, but inconsistent
specifications of the MA and AR orders are detected.  The
un-differencing assumes previous values of zero, and to remind the
user of this, those values are returned.

Random inputs for the \dQuote{burn-in} period are generated by calling
\code{rand.gen}.
\end{Details}
\begin{Value}
A time-series object of class \code{"ts"}.
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{arima}{arima}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
arima.sim(n = 63, list(ar = c(0.8897, -0.4858), ma = c(-0.2279, 0.2488)),
          sd = sqrt(0.1796))
# mildly long-tailed
arima.sim(n = 63, list(ar=c(0.8897, -0.4858), ma=c(-0.2279, 0.2488)),
          rand.gen = function(n, ...) sqrt(0.1796) * rt(n, df = 5))

# An ARIMA simulation
ts.sim <- arima.sim(list(order = c(1,1,0), ar = 0.7), n = 200)
ts.plot(ts.sim)
\end{ExampleCode}
\end{Examples}

