Arima {forecast} | R Documentation |
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
.
Arima(y, order=c(0,0,0), seasonal=c(0,0,0), xreg=NULL, include.mean=TRUE, include.drift=FALSE, include.constant, lambda=model$lambda, biasadj=FALSE, method=c("CSS-ML","ML","CSS"), model=NULL, x=y,...)
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
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)))