Description Usage Arguments Details Value Author(s) See Also Examples
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 y
.
1 2 3 4 |
y |
a univariate time series of class |
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(y)). 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 y. |
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. |
include.constant |
If TRUE, then |
lambda |
Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated. |
biasadj |
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. |
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. |
model |
Output from a previous call to |
x |
Deprecated. Included for backwards compatibility. |
... |
Additional arguments to be passed to |
See the arima
function in the stats package.
See the arima
function in the stats package. The additional objects returned are
x |
The time series data |
xreg |
The regressors used in fitting (when relevant). |
Rob J Hyndman
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | fit <- Arima(WWWusage,order=c(3,1,0))
plot(forecast(fit,h=20))
# Fit model to first few years of AirPassengers data
air.model <- Arima(window(AirPassengers,end=1956+11/12),order=c(0,1,1),
seasonal=list(order=c(0,1,1),period=12),lambda=0)
plot(forecast(air.model,h=48))
lines(AirPassengers)
# Apply fitted model to later data
air.model2 <- Arima(window(AirPassengers,start=1957),model=air.model)
# Forecast accuracy measures on the log scale.
# 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,lambda=NULL),
log(window(AirPassengers,start=1957)))
|
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