tsa.arima_process.arma_periodogram()

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.