arima0                  package:ts                  R Documentation

_A_R_I_M_A _M_o_d_e_l_l_i_n_g _o_f _T_i_m_e _S_e_r_i_e_s - _P_r_e_l_i_m_i_n_a_r_y _V_e_r_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Fit an ARIMA model to a univariate time series by exact maximum
     likelihood, and forecast from the fitted model.

_U_s_a_g_e:

     arima0(x, order = c(0, 0, 0),
            seasonal = list(order = c(0, 0, 0), period = NA),
            xreg = NULL, include.mean, na.action = na.fail,
            delta = 0.01, transform.pars = 2)

     predict(arima0.obj, n.ahead = 1, newxreg, se.fit = TRUE)

     arima0.diag(fit, gof.lag = 10) 

_A_r_g_u_m_e_n_t_s:

       x: a univariate time series

   order: A specification of the non-seasonal part of the ARIMA model:
          the three components (p, d, q) are the AR order, the degree
          of differencing, and the MA order.

seasonal: A specification of the seasonal part of the ARIMA model, plus
          the period (which defaults to `frequency(x)').

    xreg: Optionally, a vector or matrix of external regressors, which
          must have the same number of rows as `x'.

include.mean: Should the ARIMA model include a mean term? The default
          is `TRUE' for undifferenced series, `FALSE' for differenced
          ones (where a mean would not affect the fit nor predictions).

na.action: Function to be applied to remove missing values.

   delta: A value to indicate at which point `fast recursions' should
          be used. See the Details section.

transform.pars: If greater than 0, the ARMA parameters are transformed
          to ensure that they remain in the region of invertibility. If
          equal to 2, the optimization is rerun on the original scale
          to find the Hessian.

arima0.obj, fit: The result of an `arima0' fit.

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

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

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

 gof.lag: Number of lags to be used in goodness-of-fit test.

_D_e_t_a_i_l_s:

     Different definitions of ARIMA models have different signs for the
     AR and/or MA coefficients. The definition here has


 `X[t] = a[1]X[t-1] + ... + a[p]X[t-p] + e[t] + b[1]e[t-1] + ... + b[q]e[t-q]'


     and so the MA coefficients differ in sign from those of S-PLUS.
     Further, if `include.mean' is true, this formula applies to X-m
     rather than X.

     The exact likelihood is computed via a state-space representation
     of the ARMA process, and the innovations and their variance found
     by a Kalman filter using the Fortran code of Gardener et al.
     (1980).  This has the option to switch to `fast recursions'
     (assume an effectively infinite past) if the innovations variance
     is close enough to its asymptotic bound. The argument `delta' sets
     the tolerance: at its default value the approximation is normally
     negligible and the speed-up considerable. Exact computations can
     be ensured by setting `delta' to a negative value.

     The variance matrix of the estimates is found from the Hessian of
     the log-likelihood, and so may only be a rough guide, especially
     for fits close to the boundary of invertibility.

     Optimization is (currently) done by `nlm'. It will work best if
     the columns in `xreg' are roughly scaled to zero mean and unit
     variance.

     Finite-history prediction is used. This is only statistically
     efficient if the MA part of the fit is invertible, so
     `predict.arima0' will give a warning for non-invertible MA models.

_V_a_l_u_e:

     For `arima0', a list of class `"arima0"' with components:

    coef: a vector of AR, MA and regression coefficients,

  sigma2: the MLE of the innovations variance.

var.coef: the estimated variance matrix of the coefficients `coef'. If
          `transform.pars = 1', only the portion corresponding to the
          untransformed parameters is returned.

  loglik: the maximized log-likelihood (of the differenced data).

    arma: A compact form of the specification, as a vector giving the
          number of AR, MA, seasonal AR and seasonal MA coefficients,
          plus the period and the number of non-seasonal and seasonal
          differences.

     aic: the AIC value corresponding to the log-likelihood.

   resid: the residuals.

    call: the matched call.

  series: the name of the series `x'.

convergence: the value returned by `optim'.


     For `predict.arima0', 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.

_N_o_t_e:

     This is a preliminary version, and will be replaced in due course.

     The standard errors of prediction exclude the uncertainty in the
     estimation of the ARMA model and the regression coefficients.

     The results are likely to be different from S-PLUS's `arima.mle',
     which computes a conditional likelihood and does not include a
     mean in the model. Further, the convention used by `arima.mle'
     reverses the signs of the MA coefficients.

_A_u_t_h_o_r(_s):

     B.D. Ripley

_R_e_f_e_r_e_n_c_e_s:

     Brockwell, P. J. and Davis, R. A. (1996) Introduction to Time
     Series and Forecasting. Springer, New York. Sections 3.3 and 8.3.

     Gardener, G, Harvey, A. C. and Phillips, G. D. A. (1980) Algorithm
     AS154. An algorithm for exact maximum likelihood estimation of
     autoregressive-moving average models by means of Kalman filtering.
     Applied Statistics 29, 311-322.

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

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

_S_e_e _A_l_s_o:

     `ar'

_E_x_a_m_p_l_e_s:

     data(lh)
     arima0(lh, order=c(1,0,0))
     arima0(lh, order=c(3,0,0))
     arima0(lh, order=c(1,0,1))
     predict(arima0(lh, order=c(3,0,0)), n.ahead=12)

     data(USAccDeaths)
     fit <- arima0(USAccDeaths, order=c(0,1,1), seasonal=list(order=c(0,1,1)))
     fit
     predict(fit, n.ahead=6)

     data(LakeHuron)
     arima0(LakeHuron, order=c(2,0,0), xreg=1:98)

