statsmodels.sandbox.tsa.fftarma.ArmaFft.spdmapoly ArmaFft.spdmapoly(w, twosided=False) [source] ma only, need division for ar, use LagPolynomial

statsmodels.sandbox.tsa.fftarma.ArmaFft.spddirect ArmaFft.spddirect(n) [source] power spectral density using padding to length n done by fft currently returns two-sided according to fft frequencies, use first half

statsmodels.sandbox.tsa.fftarma.ArmaFft.spd ArmaFft.spd(npos) [source] raw spectral density, returns Fourier transform n is number of points in positive spectrum, the actual number of points is twice as large. different from other spd methods with fft

statsmodels.sandbox.tsa.fftarma.ArmaFft.plot4 ArmaFft.plot4(fig=None, nobs=100, nacf=20, nfreq=100) [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.periodogram ArmaFft.periodogram(nobs=None) 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 frequencie

statsmodels.sandbox.tsa.fftarma.ArmaFft.padarr ArmaFft.padarr(arr, maxlag, atend=True) [source] pad 1d array with zeros at end to have length maxlag function that is a method, no self used Parameters: arr : array_like, 1d array that will be padded with zeros maxlag : int length of array after padding atend : boolean If True (default), then the zeros are added to the end, otherwise to the front of the array Returns: arrp : ndarray zero-padded array Notes This is mainly written to

statsmodels.sandbox.tsa.fftarma.ArmaFft.pad ArmaFft.pad(maxlag) [source] construct AR and MA polynomials that are zero-padded to a common length Parameters: maxlag : int new length of lag-polynomials Returns: ar : ndarray extended AR polynomial coefficients ma : ndarray extended AR polynomial coefficients

statsmodels.sandbox.tsa.fftarma.ArmaFft.pacf ArmaFft.pacf(nobs=None) partial autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned pacf Returns: pacf : array partial autocorrelation of ARMA process given by ar, ma Notes solves yule-walker

statsmodels.sandbox.tsa.fftarma.ArmaFft.invpowerspd ArmaFft.invpowerspd(n) [source] autocovariance from spectral density scaling is correct, but n needs to be large for numerical accuracy maybe padding with zero in fft would be faster without slicing it returns 2-sided autocovariance with fftshift >>> ArmaFft([1, -0.5], [1., 0.4], 40).invpowerspd(2**8)[:10] array([ 2.08 , 1.44 , 0.72 , 0.36 , 0.18 , 0.09 , 0.045 , 0.0225 , 0.01125 , 0.005625]) &g

statsmodels.sandbox.tsa.fftarma.ArmaFft.invertroots ArmaFft.invertroots(retnew=False) make MA polynomial invertible by inverting roots inside unit circle Parameters: retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns: manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible, returned if re