code: def ar_generator(N=512, sigma=1.0):
      # this generates a signal u(n) = a1*u(n-1) + a2*u(n-2) + ... + v(n)
      # where v(n) is a stationary stochastic process with zero mean
      # and variance = sigma
      # this sequence is shown to be estimated well by an order 8 AR system
      taps = np.array([2.7607, -3.8106, 2.6535, -0.9238])
      v = np.random.normal(size=N, scale=sigma**0.5)
      u = np.zeros(N)
      P = len(taps)
      for l in range(P):
          u[l] = v[l] + np.dot(u[:l][::-1], taps[:l])
      for l in range(P, N):
          u[l] = v[l] + np.dot(u[l - P : l][::-1], taps)
      return u, v, taps
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  import matplotlib.pyplot
  from statsmodels import regression
  from statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
  from statsmodels.tsa.arima_process import arma_acovf, arma_acf
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.stattools import acf, acovf
  from statsmodels.graphics.tsaplots import plot_acf
label_desc: Use image node_id 10 for calling a global function with example usage: ar_generator(N, sigma) and returns: u,  v,  taps