arfima  R Documentation 
An ARFIMA(p,d,q) model is selected and estimated automatically using the HyndmanKhandakar (2008) algorithm to select p and q and the Haslett and Raftery (1989) algorithm to estimate the parameters including d.
arfima( y, drange = c(0, 0.5), estim = c("mle", "ls"), model = NULL, lambda = NULL, biasadj = FALSE, x = y, ... )
y 
a univariate time series (numeric vector). 
drange 
Allowable values of d to be considered. Default of

estim 
If 
model 
Output from a previous call to 
lambda 
BoxCox transformation parameter. If 
biasadj 
Use adjusted backtransformed mean for BoxCox 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. 
x 
Deprecated. Included for backwards compatibility. 
... 
Other arguments passed to 
This function combines fracdiff
and
auto.arima
to automatically select and estimate an ARFIMA
model. The fractional differencing parameter is chosen first assuming an
ARFIMA(2,d,0) model. Then the data are fractionally differenced using the
estimated d and an ARMA model is selected for the resulting time series
using auto.arima
. Finally, the full ARFIMA(p,d,q) model is
reestimated using fracdiff
. If estim=="mle"
,
the ARMA coefficients are refined using arima
.
A list object of S3 class "fracdiff"
, which is described in
the fracdiff
documentation. A few additional objects
are added to the list including x
(the original time series), and the
residuals
and fitted
values.
Rob J Hyndman and Farah Yasmeen
J. Haslett and A. E. Raftery (1989) Spacetime Modelling with Longmemory Dependence: Assessing Ireland's Wind Power Resource (with discussion); Applied Statistics 38, 150.
Hyndman, R.J. and Khandakar, Y. (2008) "Automatic time series forecasting: The forecast package for R", Journal of Statistical Software, 26(3).
fracdiff
, auto.arima
,
forecast.fracdiff
.
library(fracdiff) x < fracdiff.sim( 100, ma=.4, d=.3)$series fit < arfima(x) tsdisplay(residuals(fit))
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