statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ar ArmaFft.arma2ar(nobs=None)

statsmodels.sandbox.tsa.fftarma.ArmaFft.acovf ArmaFft.acovf(nobs=None) 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_acf, acovf Not

statsmodels.sandbox.tsa.fftarma.ArmaFft.acf2spdfreq ArmaFft.acf2spdfreq(acovf, nfreq=100, w=None) [source] not really a method just for comparison, not efficient for large n or long acf this is also similarly use in tsa.stattools.periodogram with window

statsmodels.sandbox.tsa.fftarma.ArmaFft.acf ArmaFft.acf(nobs=None) 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_acovf, acf, acovf

statsmodels.tsa.arima_model.ARMA.score ARMA.score(params) [source] Compute the score function at params. Notes This is a numerical approximation.

statsmodels.tsa.arima_model.ARMA.predict ARMA.predict(params, start=None, end=None, exog=None, dynamic=False) [source] ARMA model in-sample and out-of-sample prediction Parameters: params : array-like The fitted parameters of the model. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to

statsmodels.tsa.arima_model.ARMA.loglike_kalman ARMA.loglike_kalman(params, set_sigma2=True) [source] Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.tsa.arima_model.ARMA.loglike_css ARMA.loglike_css(params, set_sigma2=True) [source] Conditional Sum of Squares likelihood function.

statsmodels.tsa.arima_model.ARMA.loglike ARMA.loglike(params, set_sigma2=True) [source] Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.tsa.arima_model.ARMA.initialize ARMA.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.