statsmodels.tsa.arima_process.arma_periodogram

statsmodels.tsa.arima_process.arma_periodogram(ar, ma, worN=None, whole=0) [source]

Periodogram for ARMA process given by lag-polynomials ar and ma

Parameters:

ar (array_like) – autoregressive lag-polynomial with leading 1 and lhs sign ma (array_like) – moving average lag-polynomial with leading 1 worN ({None, int}, optional) – option for scipy.signal.freqz (read “w or N”) If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencies. Otherwise, compute the response at frequencies given in worN whole ({0,1}, optional) – options for scipy.signal.freqz Normally, frequencies are computed from 0 to pi (upper-half of unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi.

Returns:

w (array) – frequencies sd (array) – periodogram, spectral density

Notes

Normalization ?

This uses signal.freqz, which does not use fft. There is a fft version somewhere.