pysteps.timeseries.autoregression.iterate_ar_model

pysteps.timeseries.autoregression.iterate_ar_model(X, phi, EPS=None)

Apply an AR(p) model to a time-series of two-dimensional fields.

Parameters:

X : array_like

Three-dimensional array of shape (p,w,h) containing a time series of p two-dimensional fields of shape (w,h). The fields are assumed to be in ascending order by time, and the timesteps are assumed to be regular.

phi : array_like

Array of length p+1 specifying the parameters of the AR(p) model. The parameters are in ascending order by increasing time lag, and the last element is the parameter corresponding to the innovation term EPS.

EPS : array_like

Optional perturbation field for the AR(p) process. If EPS is None, the innovation term is not added.