| arima {stats} | R Documentation |
Fit an ARIMA model to a univariate time series.
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.method = "BFGS",
optim.control = list(), kappa = 1e6)
x |
a univariate time series |
order |
A specification of the non-seasonal part of the ARIMA
model: the three components |
seasonal |
A specification of the seasonal part of the ARIMA
model, plus the period (which defaults to |
xreg |
Optionally, a vector or matrix of external regressors,
which must have the same number of rows as |
include.mean |
Should the ARMA model include a mean/intercept term? The
default is |
transform.pars |
Logical. If true, the AR parameters are
transformed to ensure that they remain in the region of
stationarity. Not used for |
fixed |
optional numeric vector of the same length as the total
number of parameters. If supplied, only |
init |
optional numeric vector of initial parameter
values. Missing values will be filled in, by zeroes except for
regression coefficients. Values already specified in |
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. |
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. |
optim.method |
The value passed as the |
optim.control |
List of control parameters for |
kappa |
the prior variance (as a multiple of the innovations variance) for the past observations in a differenced model. Do not reduce this. |
Different definitions of ARMA models have different signs for the AR and/or MA coefficients. The definition used 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 (the default for an ARMA
model), this formula applies to X - m rather than X. For
ARIMA models with differencing, the differenced series follows a
zero-mean ARMA model. If am xreg term is included, a linear
regression (with a constant term if include.mean is true and
there is no differencing) is fitted with an ARMA model for the error
term.
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 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.
A list of class "Arima" with components:
coef |
a vector of AR, MA and regression coefficients, which can
be extracted by the |
sigma2 |
the MLE of the innovations variance. |
var.coef |
the estimated variance matrix of the coefficients
|
loglik |
the maximized log-likelihood (of the differenced data), or the approximation to it used. |
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. Only
valid for |
residuals |
the fitted innovations. |
call |
the matched call. |
series |
the name of the series |
code |
the convergence value returned by |
n.cond |
the number of initial observations not used in the fitting. |
model |
A list representing the Kalman Filter used in the
fitting. See |
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 et al. (1980). For a
differenced process the non-stationary components are given a diffuse
prior (controlled by kappa). Observations which are still
controlled by the diffuse prior (determined by having a Kalman gain of
at least 1e4) are excluded from the likelihood calculations.
(This gives comparable results to 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 "ML".
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 not constrained to be
invertible during optimization, but they will be converted to
invertible form after optimization if 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 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.
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.
arima is very similar to arima0 for
ARMA models or for differenced models without missing values,
but handles differenced models with missing values exactly.
It is somewhat slower than arima0, particularly for seasonally
differenced models.
Brockwell, P. J. and Davis, R. A. (1996) Introduction to Time Series and Forecasting. Springer, New York. Sections 3.3 and 8.3.
Durbin, J. and Koopman, S. J. (2001) 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. Applied Statistics 29, 311–322.
Harvey, A. C. (1993) 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. Technometrics 20 389–395.
predict.Arima, arima.sim for simulating
from an ARIMA model, tsdiag, arima0,
ar
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")
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
arima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron)-1920)
## presidents contains NAs
## graphs in example(acf) suggest order 1 or 3
require(graphics)
(fit1 <- arima(presidents, c(1, 0, 0)))
tsdiag(fit1)
(fit3 <- arima(presidents, c(3, 0, 0))) # smaller AIC
tsdiag(fit3)