statsmodels.tsa.arima_process.ArmaProcess.from_estimation classmethod ArmaProcess.from_estimation(model_results, nobs=None) [source] Create ArmaProcess instance from ARMA estimation results Parameters: model_results : ARMAResults instance A fitted model nobs : int, optional If None, nobs is taken from the results

statsmodels.tsa.arima_process.ArmaProcess.from_coeffs classmethod ArmaProcess.from_coeffs(arcoefs, macoefs, nobs=100) [source] Create ArmaProcess instance from coefficients of the lag-polynomials Parameters: arcoefs : array-like Coefficient for autoregressive lag polynomial, not including zero lag. The sign is inverted to conform to the usual time series representation of an ARMA process in statistics. See the class docstring for more information. macoefs : array-like Coefficient for mov

statsmodels.tsa.arima_process.ArmaProcess.arma2ma ArmaProcess.arma2ma(nobs=None) [source]

statsmodels.tsa.arima_process.ArmaProcess.arma2ar ArmaProcess.arma2ar(nobs=None) [source]

statsmodels.tsa.arima_process.ArmaProcess.acovf ArmaProcess.acovf(nobs=None) [source] theoretical autocovariance function of 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 acovf Returns: acovf : array autocovariance of ARMA process given by ar, ma See also arma_

statsmodels.tsa.arima_process.ArmaProcess.acf ArmaProcess.acf(nobs=None) [source] theoretical 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 acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_aco

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

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdroots ArmaFft.spdroots_(arroots, maroots, w) [source] spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies) Parameters: arroots : ndarray roots of ar (denominator) lag-polynomial maroots : ndarray roots of ma (numerator) lag-polynomial w : array_like frequencies for which spd is calculated Notes this should go into a function

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdroots ArmaFft.spdroots(w) [source] spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies)

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdpoly ArmaFft.spdpoly(w, nma=50) [source] spectral density from MA polynomial representation for ARMA process References Cochrane, section 8.3.3