pysteps.timeseries.autoregression.estimate_ar_params_yw_localized(gamma, d=0)#

Estimate the parameters of a localized AR(p) model


from the Yule-Walker equations using the given set of autocorrelation coefficients :math`gamma_{l,i}`, where :math`l` denotes time lag and \(i\) denote spatial coordinates with arbitrary dimension.

  • gamma (array_like) – A list containing the lag-l temporal autocorrelation coefficient fields for l=1,2,…p. The correlation coefficients are assumed to be in ascending order with respect to time lag.

  • d ({0,1}) – The order of differencing. If d=1, the correlation coefficients gamma are assumed to be computed from the differenced time series, which is also done for the resulting parameter estimates.


out – List of length p+1 containing the AR(p) parameter fields for for the lag-p terms and the innovation term. The parameter fields have the same shape as the elements of gamma.

Return type:



To estimate the parameters of an integrated ARI(p,d) model, compute the correlation coefficients gamma by calling pysteps.timeseries.correlation.temporal_autocorrelation() with d>0 and window_radius<np.inf.