arima.sim
Simulate from an ARIMA Model
Simulate from an ARIMA model.
 Keywords
 ts
Usage
arima.sim(model, n, rand.gen = rnorm, innov = rand.gen(n, ...), n.start = NA, start.innov = rand.gen(n.start, ...), ...)
Arguments
 model
 A list with component
ar
and/orma
giving the AR and MA coefficients respectively. Optionally a componentorder
can be used. An empty list gives an ARIMA(0, 0, 0) model, that is white noise.  n
 length of output series, before undifferencing. A strictly positive integer.
 rand.gen
 optional: a function to generate the innovations.
 innov
 an optional times series of innovations. If not
provided,
rand.gen
is used.  n.start
 length of ‘burnin’ period. If
NA
, the default, a reasonable value is computed.  start.innov
 an optional times series of innovations to be used
for the burnin period. If supplied there must be at least
n.start
values (andn.start
is by default computed inside the function).  ...
 additional arguments for
rand.gen
. Most usefully, the standard deviation of the innovations generated byrnorm
can be specified bysd
.
Details
See arima
for the precise definition of an ARIMA model.
The ARMA model is checked for stationarity.
ARIMA models are specified via the order
component of
model
, in the same way as for arima
. Other
aspects of the order
component are ignored, but inconsistent
specifications of the MA and AR orders are detected. The
undifferencing assumes previous values of zero, and to remind the
user of this, those values are returned.
Random inputs for the ‘burnin’ period are generated by calling
rand.gen
.
Value

A timeseries object of class
"ts"
.
See Also
Examples
library(stats)
require(graphics)
arima.sim(n = 63, list(ar = c(0.8897, 0.4858), ma = c(0.2279, 0.2488)),
sd = sqrt(0.1796))
# mildly longtailed
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)
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