predict.Arima             package:stats             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:

     ## S3 method for class 'Arima':
     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.

_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:

     predict(arima(lh, order = c(3,0,0)), n.ahead = 12)

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