pysteps.timeseries.autoregression.iterate_ar_model(x, phi, eps=None)#

Apply an AR(p) model

\(x_{k+1}=\phi_1 x_k+\phi_2 x_{k-1}+\dots+\phi_p x_{k-p}+\phi_{p+1}\epsilon\)

to a time series \(x_k\).

  • x (array_like) – Array of shape (n,…), n>=p, containing a time series of a input variable x. The elements of x along the first dimension are assumed to be in ascending order by time, and the time intervals are assumed to be regular.

  • phi (list) – List or 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 innovation term for the AR(p) process. The shape of eps is expected to be a scalar or x.shape[1:] if len(x.shape)>1. If eps is None, the innovation term is not added.