An ARFIMA(p,d,q) model is selected and estimated automatically using the Hyndman-Khandakar (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, ...)



a univariate time series (numeric vector).


Allowable values of d to be considered. Default of c(0,0.5) ensures a stationary model is returned.


If estim=="ls", then the ARMA parameters are calculated using the Haslett-Raftery algorithm. If estim=="mle", then the ARMA parameters are calculated using full MLE via the arima function.


Output from a previous call to arfima. If model is passed, this same model is fitted to y without re-estimating any parameters.


Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated.


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.


Deprecated. Included for backwards compatibility.

Other arguments passed to auto.arima when selecting p and q.


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.


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 re-estimated using fracdiff. If estim=="mle", the ARMA coefficients are refined using arima.


J. Haslett and A. E. Raftery (1989) Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource (with discussion); Applied Statistics 38, 1-50.

Hyndman, R.J. and Khandakar, Y. (2008) "Automatic time series forecasting: The forecast package for R", Journal of Statistical Software, 26(3).

See also

fracdiff, auto.arima, forecast.fracdiff.


library(fracdiff) x <- fracdiff.sim( 100, ma=-.4, d=.3)$series fit <- arfima(x) tsdisplay(residuals(fit))