predict.Arima               package:ts               R Documentation

_F_o_r_e_c_a_s_t _f_r_o_m _A_R_I_M_A _f_i_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Forecast from models fitted by `arima'.

_U_s_a_g_e:

     predict(object, n.ahead = 1, newxreg = NULL,
          se.fit = TRUE, ...)

_A_r_g_u_m_e_n_t_s:

  object: The result of an `arima' fit.

 n.ahead: The number of steps ahead for which prediction is required.

 newxreg: New values of `xreg' to be used for prediction. Must have at
          least `n.ahead' rows.

  se.fit: Logical: should standard errors of prediction be returned?

     ...: arguments passed to or from other methods.

_D_e_t_a_i_l_s:

     Finite-history prediction is used, via `KalmanForecast'. This is
     only statistically efficient if the MA part of the fit is
     invertible, so `predict.Arima' will give a warning for
     non-invertible MA models.

     The standard errors of prediction exclude the uncertainty in the
     estimation of the ARMA model and the regression coefficients.
     According to Harvey (1993, pp. 58-9) the effect is small.

_V_a_l_u_e:

     A time series of predictions, or if `se.fit = TRUE', a list with
     components `pred', the predictions, and `se', the estimated
     standard errors.  Both components are time series.

_A_u_t_h_o_r(_s):

     B. D. Ripley

_R_e_f_e_r_e_n_c_e_s:

     Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State
     Space Methods.  Oxford University Press.

     Harvey, A. C. and McKenzie, C. R. (1982) Algorithm AS182. An
     algorithm for finite sample prediction from ARIMA processes.
     Applied Statistics 31, 180-187.

     Harvey, A. C. (1993) Time Series Models, 2nd Edition, Harvester
     Wheatsheaf, sections 3.3 and 4.4.

_S_e_e _A_l_s_o:

     `arima'

_E_x_a_m_p_l_e_s:

     data(lh, package="ts")
     predict(arima(lh, order = c(3,0,0)), n.ahead = 12)

     data(USAccDeaths, package="ts")
     (fit <- arima(USAccDeaths, order = c(0,1,1),
                   seasonal = list(order=c(0,1,1))))
     predict(fit, n.ahead = 6)

