Arima | 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 numerical vector or matrix of external regressors, which must have the same number of rows as y. It should not be a data frame. |
include.mean |
Should the ARIMA model include a mean term? The default
is |
include.drift |
Should the ARIMA model include a linear drift term?
(i.e., a linear regression with ARIMA errors is fitted.) The default is
|
include.constant |
If |
lambda |
Box-Cox transformation parameter. If |
biasadj |
Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. |
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). |
sigma2 |
The bias adjusted MLE of the innovations variance. |
Rob J Hyndman
auto.arima
, forecast.Arima
.
library(ggplot2)
WWWusage %>%
Arima(order=c(3,1,0)) %>%
forecast(h=20) %>%
autoplot
# 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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