\Header{arima}{ARIMA Modelling of Time Series}
\alias{print.Arima}{arima}
\keyword{ts}{arima}
\begin{Description}\relax
Fit an ARIMA model to a univariate time series.\end{Description}
\begin{Usage}
\begin{verbatim}
arima(x, order = c(0, 0, 0),
      seasonal = list(order = c(0, 0, 0), period = NA),
      xreg = NULL, include.mean = TRUE, transform.pars = TRUE,
      fixed = NULL, init = NULL, method = c("CSS-ML", "ML", "CSS"),
      n.cond, optim.control = list(), kappa = 1e6)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a univariate time series
\item[\code{order}] A specification of the non-seasonal part of the ARIMA
model: the three components \eqn{(p, d, q)}{} are the AR order, the
degree of differencing, and the MA order.
\item[\code{seasonal}] A specification of the seasonal part of the ARIMA
model, plus the period (which defaults to \code{frequency(x)}).
This should be a list with components \code{order} and
\code{period}, but a specification of just a numeric vector of
length 3 will be turned into a suitable list with the specification
as the \code{order}.
\item[\code{xreg}] Optionally, a vector or matrix of external regressors,
which must have the same number of rows as \code{x}.
\item[\code{include.mean}] Should the ARIMA model include
a mean term? The default is \code{TRUE} for undifferenced series,
\code{FALSE} for differenced ones (where a mean would not affect
the fit nor predictions).
\item[\code{transform.pars}] Logical.  If true, the AR parameters are
transformed to ensure that they remain in the region of
stationarity.  Not used for \code{method = "CSS"}.
\item[\code{fixed}] optional numeric vector of the same length as the total
number of parameters.  If supplied, only non-\code{NA} entries in
\code{fixed} will be varied.  \code{transform.pars = TRUE}
will be overridden if any AR parameters are fixed.
\item[\code{init}] optional numeric vector of initial parameter
values. Missing values will be filled in, by zeroes except for
regression coefficients.
\item[\code{method}] Fitting method: maximum likelihood or minimize
conditional sum-of-squares.  The default (unless there are missing
values) is to use conditional-sum-of-squares to find starting
values, then maximum likelihood.
\item[\code{n.cond}] 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.
\item[\code{optim.control}] List of control parameters for \code{\Link{optim}}.
\item[\code{kappa}] the prior variance (as a multiple of the innovations
variance) for the past observations in a differenced model.  Do not
reduce this.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Different definitions of ARMA models have different signs for the
AR and/or MA coefficients. The definition here has

\deqn{X_t = a_1X_{t-1} + \cdots + a_pX_{t-p} + e_t + b_1e_{t-1} + \dots + b_qe_{t-q}}{\code{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 \code{include.mean} is true, this formula
applies to \eqn{X-m}{} rather than \eqn{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.

Optimization is done by \code{\Link{optim}}. It will work
best if the columns in \code{xreg} are roughly scaled to zero mean
and unit variance, but does attempt to estimate suitable scalings.\end{Details}
\begin{Value}
A list of class \code{"Arima"} with components:

\begin{ldescription}
\item[\code{coef}] a vector of AR, MA and regression coefficients,
\item[\code{sigma2}] the MLE of the innovations variance.
\item[\code{var.coef}] the estimated variance matrix of the coefficients
\code{coef}.
\item[\code{loglik}] the maximized log-likelihood (of the differenced data),
or the approximation to it used.
\item[\code{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.
\item[\code{aic}] the AIC value corresponding to the log-likelihood. Only
valid for \code{method = "ML"} fits.
\item[\code{residuals}] the standardized residuals.
\item[\code{call}] the matched call.
\item[\code{series}] the name of the series \code{x}.
\item[\code{convergence}] the value returned by \code{\Link{optim}}.
\item[\code{n.cond}] the number of initial observations not used in the fitting.
\item[\code{model}] A list representing the Kalman Filter used in the
fitting.  See \code{\Link{KalmanLike}}.
\end{ldescription}
\end{Value}
\begin{Section}{Fitting methods}
The exact likelihood is computed via a state-space representation of
the ARIMA process, and the innovations and their variance found by a
Kalman filter.  The initialization of the differenced ARMA process uses
stationarity and is based on Gardner \emph{et al.} (1980).  For a
differenced process the non-stationary components are given a diffuse
prior (controlled by \code{kappa}).  Observations which are still
controlled by the diffuse prior (determined by having a Kalman gain of
at least \code{1e4}) are excluded from the likelihood calculations.
(This gives comparable results to \code{\Link{arima0}} in the absence
of missing values, when the observations excluded are precisely those
dropped by the differencing.)

Missing values are allowed, and are handled exactly in method \code{"ML"}.

If \code{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 not constrained to be
invertible during optimization, but they will be converted to
invertible form after optimization if \code{transform.pars} is true.

Conditional sum-of-squares is provided mainly for expositional
purposes.  This computes the sum of squares of the fitted innovations
from observation \code{n.cond} on, (where \code{n.cond} is at least
the maximum lag of an AR term), treating all earlier innovations to
be zero.  Argument \code{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.\end{Section}
\begin{Note}\relax
The results are likely to be different from S-PLUS's
\code{arima.mle}, which computes a conditional likelihood and does
not include a mean in the model.  Further, the convention used by
\code{arima.mle} reverses the signs of the MA coefficients.

\code{arima} is very similar to \code{\Link{arima0}} for
ARMA models or for differenced models without missing values,
but handles differenced models with missing values exactly.
It is somewhat slower than \code{arima0}, particularly for seasonally
differenced models.\end{Note}
\begin{Author}\relax
B. D. Ripley\end{Author}
\begin{References}\relax
Brockwell, P. J. and Davis, R. A. (1996) \emph{Introduction to Time
Series and Forecasting.} Springer, New York. Sections 3.3 and 8.3.

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

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.
\emph{Applied Statistics} \bold{29}, 311--322.

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

Jones, R. H. (1980) Maximum likelihood fitting of ARMA models to time
series with missing observations. \emph{Technometrics} \bold{20} 389--395.\end{References}
\begin{SeeAlso}\relax
\code{\Link{predict.Arima}}, \code{\Link{tsdiag}},
\code{\Link{arima0}}, \code{\Link{ar}}\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(lh)
arima(lh, order = c(1,0,0))

arima(lh, order = c(3,0,0))
arima(lh, order = c(1,0,1))

arima(lh, order = c(3,0,0), method = "CSS")

data(USAccDeaths)
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)))
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)),
      method = "CSS") # drops first 13 observations.
# for a model with as few years as this, we want full ML

data(LakeHuron)
arima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron)-1920)

data(presidents) # contains NAs
## graphs in example(acf) suggest order 1 or 3
(fit1 <- arima(presidents, c(1, 0, 0)))
tsdiag(fit1)
(fit3 <- arima(presidents, c(3, 0, 0)))  # smaller AIC
tsdiag(fit3)
\end{ExampleCode}
\end{Examples}

