# Arima

0th

Percentile

##### Fit ARIMA model to univariate time series

Largely a wrapper for the arima function in the stats package. The main difference is that this function allows a drift term. It is also possible to take an ARIMA model from a previous call to Arima and re-apply it to the data x.

Keywords
ts
##### Usage
Arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0, 0), period = NA),
xreg = NULL, include.mean = TRUE, include.drift = FALSE,
transform.pars = TRUE, fixed = NULL, init = NULL,
method = c("CSS-ML", "ML", "CSS"), n.cond,
optim.control = list(), kappa = 1e6, model=NULL)
##### Arguments
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)). 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
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).
include.drift
Should the ARIMA model include a linear drift term? (i.e., a linear regression with ARIMA errors is fitted.) The default is FALSE.
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".
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.p
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.
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.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.
model
Output from a previous call to Arima. If model is passed, this same model is fitted to x without re-estimating any parameters.
##### Details

See the arima function in the stats package.

##### Value

• See the arima function in the stats package. The additional objects returned are
• xThe time series data
• xregThe regressors used in fitting (when relevant).

arima

• Arima
##### Examples
fit <- Arima(WWWusage,order=c(3,1,0))
plot(forecast(fit,h=20))

air.model <- Arima(window(AirPassengers,end=1956+11/12),order=c(0,1,1),seasonal=list(order=c(0,1,1),period=12))
plot(forecast(air.model,h=48))
lines(AirPassengers)

air.model2 <- Arima(window(AirPassengers,start=1957),model=air.model)
outofsample <- fitted(air.model2)
# in-sample one-step forecasts
accuracy(air.model)
# out-of-sample one-step forecasts
accuracy(air.model2)
# out-of-sample multi-step forecasts
accuracy(forecast(air.model,h=48),outofsample)
Documentation reproduced from package forecast, version 2.19, License: GPL (>= 2)

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