arima.sim                 package:ts                 R Documentation

_S_i_m_u_l_a_t_e _f_r_o_m _a_n _A_R_I_M_A _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Simulate from an ARIMA model.

_U_s_a_g_e:

     arima.sim(model, n, rand.gen = rnorm, innov = rand.gen(n, ...),
               n.start = NA, ...)

_A_r_g_u_m_e_n_t_s:

   model: A list with component `ar' and/or `ma' giving the AR and MA
          coeffcients respectively.  Optionally a component `order' can
          be used.

       n: length of output series.

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 ``burn-in'' period.  If `NA', the default, a
          reasonable value is computed.

     ...: additional arguments for `rand.gen'.  Most usefully, the
          standard deviation of the innovations generated by `rnorm'
          can be specified by `sd'.

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     A time-series object of class `"ts"'.

_S_e_e _A_l_s_o:

     `arima.sim'

_E_x_a_m_p_l_e_s:

     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)

