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
arand/ormagiving the AR and MA coefficients respectively. Optionally a componentordercan be used. An empty list gives an ARIMA(0, 0, 0) model, that is white noise.- n
length of output series, before un-differencing. A strictly positive integer.
- rand.gen
optional: a function to generate the innovations.
- innov
an optional times series of innovations. If not provided,
rand.genis used.- n.start
length of ‘burn-in’ period. If
NA, the default, a reasonable value is computed.- start.innov
an optional times series of innovations to be used for the burn-in period. If supplied there must be at least
n.startvalues (andn.startis by default computed inside the function).- …
additional arguments for
rand.gen. Most usefully, the standard deviation of the innovations generated byrnormcan 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
un-differencing assumes previous values of zero, and to remind the
user of this, those values are returned.
Random inputs for the ‘burn-in’ period are generated by calling
rand.gen.
Value
A time-series object of class "ts".
See Also
Examples
library(stats)
# NOT RUN {
require(graphics)
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)
# }