arfima

0th

Percentile

Fit a fractionally differenced ARFIMA model

An ARFIMA(p,d,q) model is selected and estimated automatically using the Hyndman-Khandakar (2008) algorithm to select p and q and maximum likelihood estimation based on Haslett and Raftery (1989) to estimate the parameters including d.

Keywords
ts
Usage
arfima(x, drange = c(0, 0.5), ...)
Arguments
x
a univariate time series (numeric vector).
drange
Allowable values of d to be considered. Default of c(0,0.5) ensures a stationary model is returned.
...
Other arguments passed to auto.arima when selecting p and q.
Details

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.

Value

  • 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.

References

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.

Aliases
  • arfima
Examples
x <- fracdiff.sim( 100, ma = -.4, d = .3)$series
fit <- arfima(x)
tsdisplay(residuals(fit))
Documentation reproduced from package forecast, version 2.05, License: GPL (>= 2)

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