Fit an ARIMA model to a univariate time series.

```
arima(x, order = c(0L, 0L, 0L),
seasonal = list(order = c(0L, 0L, 0L), period = NA),
xreg = NULL, include.mean = TRUE,
transform.pars = TRUE,
fixed = NULL, init = NULL,
method = c("CSS-ML", "ML", "CSS"), n.cond,
SSinit = c("Gardner1980", "Rossignol2011"),
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 integer 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)`

).
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`

.

xreg

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

.

include.mean

Should the ARMA model include a mean/intercept term? The
default is `TRUE`

for undifferenced series, and it is ignored
for ARIMA models with differencing.

transform.pars

logical; if true, the AR parameters are
transformed to ensure that they remain in the region of
stationarity. Not used for `method = "CSS"`

. For
`method = "ML"`

, it has been advantageous to set
`transform.pars = FALSE`

in some cases, see also `fixed`

.

fixed

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 AR parameters are fixed.
It may be wise to set `transform.pars = FALSE`

when fixing
MA parameters, especially near non-invertibility.

init

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.

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. Can be abbreviated.

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.

SSinit

a string specifying the algorithm to compute the
state-space initialization of the likelihood; see
`KalmanLike`

for details. Can be abbreviated.

optim.method

The value passed as the `method`

argument to
`optim`

.

optim.control

List of control parameters for `optim`

.

kappa

the prior variance (as a multiple of the innovations variance) for the past observations in a differenced model. Do not reduce this.

A list of class `"Arima"`

with components:

a vector of AR, MA and regression coefficients, which can
be extracted by the `coef`

method.

the MLE of the innovations variance.

the estimated variance matrix of the coefficients
`coef`

, which can be extracted by the `vcov`

method.

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 convergence value returned by `optim`

.

the number of initial observations not used in the fitting.

the number of “used” observations for the fitting,
can also be extracted via `nobs()`

and is used by
`BIC`

.

A list representing the Kalman Filter used in the
fitting. See `KalmanLike`

.

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.

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

$$X_t= a_1X_{t-1}+ \cdots+ a_pX_{t-p} + e_t + b_1e_{t-1}+\cdots+ b_qe_{t-q} $$

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.

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 AS 154: An algorithm for exact maximum likelihood estimation
of autoregressive-moving average models by means of Kalman filtering.
*Applied Statistics*, **29**, 311--322.
10.2307/2346910.

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*, **22**, 389--395.
10.2307/1268324.

Ripley, B. D. (2002) Time series in R 1.5.0.
*R News*, **2/2**, 2--7.
https://www.r-project.org/doc/Rnews/Rnews_2002-2.pdf

`predict.Arima`

, `arima.sim`

for simulating
from an ARIMA model, `tsdiag`

, `arima0`

,
`ar`

```
# NOT RUN {
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)))
nobs(fit1)
tsdiag(fit1)
(fit3 <- arima(presidents, c(3, 0, 0))) # smaller AIC
tsdiag(fit3)
BIC(fit1, fit3)
## compare a whole set of models; BIC() would choose the smallest
AIC(fit1, arima(presidents, c(2,0,0)),
arima(presidents, c(2,0,1)), # <- chosen (barely) by AIC
fit3, arima(presidents, c(3,0,1)))
## An example of ARIMA forecasting:
predict(fit3, 3)
# }
```

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