Usage
autoarfima(data, ar.max = 2, ma.max = 2, criterion = c("AIC","BIC","SIC","HQIC"),
method = c("partial", "full"), arfima = FALSE, include.mean = NULL,
distribution.model = "norm", cluster = NULL, external.regressors = NULL,
solver = "solnp", solver.control=list(), fit.control=list(), return.all = FALSE)Arguments
data
A univariate data object. Can be a numeric vector, matrix,
data.frame, zoo, xts, timeSeries, ts or irts object.
ar.max
Maximum AR order to test for.
ma.max
Maximum MA order to test for.
criterion
Information Criterion to use for selecting the best model.
method
The partial method tests combinations of consecutive orders of AR and
MA i.e. 1:2, 1:3 etc, while the full method tests all possible combinations
within the consecutive orders thus enumerating the complete combination space
of the MA and AR orders.
.
arfima
Can be TRUE, FALSE or NULL in which case it is tested.
include.mean
Can be TRUE, FALSE or NULL in which case it is tested.
cluster
A cluster object created by calling makeCluster from the parallel
package. If it is not NULL, then this will be used for parallel estimation.
external.regressors
A matrix object containing the external regressors to
include in the mean equation with as many rows as will be included in the
data (which is passed in the fit function).
distribution.model
The distribution density to use for the innovations (defaults to Normal).
solver
One of either nlminb, solnp, gosolnp
or nloptr.
solver.control
Control arguments list passed to optimizer.
fit.control
Control arguments passed to the fitting routine.
return.all
Whether to return all the fitted models or only the best one.