Fit an ARIMA model to a univariate time series, and forecast from the fitted model.
arima0(x, order = c(0, 0, 0),
seasonal = list(order = c(0, 0, 0), period = NA),
xreg = NULL, include.mean = TRUE, delta = 0.01,
transform.pars = TRUE, fixed = NULL, init = NULL,
method = c("ML", "CSS"), n.cond, optim.control = list())# S3 method for arima0
predict(object, n.ahead = 1, newxreg, se.fit = TRUE, …)
a univariate time series
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.
A specification of the seasonal part of the ARIMA
model, plus the period (which defaults to frequency(x)).
This should be a list with components order and
period, but a specification of just a numeric vector of
length 3 will be turned into a suitable list with the specification
as the order.
Optionally, a vector or matrix of external regressors,
which must have the same number of rows as x.
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).
A value to indicate at which point ‘fast recursions’ should be used. See the ‘Details’ section.
Logical. If true, the AR parameters are
transformed to ensure that they remain in the region of
stationarity. Not used for method = "CSS".
optional numeric vector of the same length as the total
number of parameters. If supplied, only NA entries in
fixed will be varied. transform.pars = TRUE
will be overridden (with a warning) if any ARMA parameters are
fixed.
optional numeric vector of initial parameter
values. Missing values will be filled in, by zeroes except for
regression coefficients. Values already specified in fixed
will be ignored.
Fitting method: maximum likelihood or minimize conditional sum-of-squares. Can be abbreviated.
Only used if fitting by conditional-sum-of-squares: the number of initial observations to ignore. It will be ignored if less than the maximum lag of an AR term.
List of control parameters for optim.
The result of an arima0 fit.
New values of xreg to be used for
prediction. Must have at least n.ahead rows.
The number of steps ahead for which prediction is required.
Logical: should standard errors of prediction be returned?
arguments passed to or from other methods.
For arima0, a list of class "arima0" with components:
a vector of AR, MA and regression coefficients,
the MLE of the innovations variance.
the estimated variance matrix of the coefficients
coef.
the maximized log-likelihood (of the differenced data), or the approximation to it used.
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.
the AIC value corresponding to the log-likelihood. Only
valid for method = "ML" fits.
the fitted innovations.
the matched call.
the name of the series x.
the value returned by optim.
the number of initial observations not used in the fitting.
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.
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 based on Gardner 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.
If transform.pars is true, the optimization is done using an
alternative parametrization which is a variation on that suggested by
Jones (1980) and ensures that the model is stationary. For an AR(p)
model the parametrization is via the inverse tanh of the partial
autocorrelations: the same procedure is applied (separately) to the
AR and seasonal AR terms. The MA terms are also constrained to be
invertible during optimization by the same transformation if
transform.pars is true. Note that the MLE for MA terms does
sometimes occur for MA polynomials with unit roots: such models can be
fitted by using transform.pars = FALSE and specifying a good
set of initial values (often obtainable from a fit with
transform.pars = TRUE).
Missing values are allowed, but any missing values
will force delta to be ignored and full recursions used.
Note that missing values will be propagated by differencing, so the
procedure used in this function is not fully efficient in that case.
Conditional sum-of-squares is provided mainly for expositional
purposes. This computes the sum of squares of the fitted innovations
from observation
n.cond on, (where n.cond is at least the maximum lag of
an AR term), treating all earlier innovations to be zero. Argument
n.cond can be used to allow comparability between different
fits. The ‘part log-likelihood’ is the first term, half the
log of the estimated mean square. Missing values are allowed, but
will cause many of the innovations to be missing.
When regressors are specified, they are orthogonalized prior to fitting unless any of the coefficients is fixed. It can be helpful to roughly scale the regressors to zero mean and unit variance.
Different definitions of ARMA models have different signs for the AR and/or MA coefficients. The definition here has
$$X_t = a_1X_{t-1} + \cdots + a_pX_{t-p} + e_t + b_1e_{t-1} + \dots + b_qe_{t-q}$$
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 \(X\). For ARIMA models with
differencing, the differenced series follows a zero-mean ARMA model.
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 done by optim. It will work
best if the columns in xreg are roughly scaled to zero mean
and unit variance, but does attempt to estimate suitable scalings.
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.
Brockwell, P. J. and Davis, R. A. (1996) Introduction to Time Series and Forecasting. Springer, New York. Sections 3.3 and 8.3.
Gardner, 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, sections 3.3 and 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.
Jones, R. H. (1980) Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics 22 389--395.
# NOT RUN {
arima0(lh, order = c(1,0,0))
# }
# NOT RUN {
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)
arima0(lh, order = c(3,0,0), method = "CSS")
# for a model with as few years as this, we want full ML
(fit <- arima0(USAccDeaths, order = c(0,1,1),
seasonal = list(order=c(0,1,1)), delta = -1))
predict(fit, n.ahead = 6)
arima0(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron)-1920)
# }
# NOT RUN {
## presidents contains NAs
## graphs in example(acf) suggest order 1 or 3
(fit1 <- arima0(presidents, c(1, 0, 0), delta = -1)) # avoid warning
tsdiag(fit1)
(fit3 <- arima0(presidents, c(3, 0, 0), delta = -1)) # smaller AIC
tsdiag(fit3)
# }
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