statsmodels.tsa.arima_model.ARMAResults class statsmodels.tsa.arima_model.ARMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an ARMA model. Parameters: model : ARMA instance The fitted model instance params : array Fitted parameters normalized_cov_params : array, optional The normalized variance covariance matrix scale : float, optional Optional argument to scale the variance covariance matrix. Returns: **Attributes** : ai

statsmodels.tsa.arima_process.arma2ar statsmodels.tsa.arima_process.arma2ar(ar, ma, nobs=100) [source] get the AR representation of an ARMA process Parameters: ar : array_like, 1d auto regressive lag polynomial ma : array_like, 1d moving average lag polynomial nobs : int number of observations to calculate Returns: ar : array, 1d coefficients of AR lag polynomial with nobs elements ` : Notes This is just an alias for ar_representation = arma_impulse_response(ma, ar, nobs=100) ful

statsmodels.tsa.arima_process.ar2arma statsmodels.tsa.arima_process.ar2arma(ar_des, p, q, n=20, mse='ar', start=None) [source] find arma approximation to ar process This finds the ARMA(p,q) coefficients that minimize the integrated squared difference between the impulse_response functions (MA representation) of the AR and the ARMA process. This does currently not check whether the MA lagpolynomial of the ARMA process is invertible, neither does it check the roots of the AR lagpolynomial. Par

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

statsmodels.tsa.arima_model.ARIMAResults class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Methods aic() arfreq() Returns the frequency of the AR roots. arparams() arroots() bic() bse() conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params() f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. fittedvalues() forecast([steps, exog,

statsmodels.tsa.arima_model.ARIMA class statsmodels.tsa.arima_model.ARIMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Integrated Moving Average ARIMA(p,d,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or

statsmodels.robust.norms.TrimmedMean.weights TrimmedMean.weights(z) [source] Least trimmed mean weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = 1 for |z| <= c weights(z) = 0 for |z| > c

statsmodels.robust.norms.TrimmedMean.psi_deriv TrimmedMean.psi_deriv(z) [source] The derivative of least trimmed mean psi function Notes Used to estimate the robust covariance matrix.

statsmodels.robust.norms.TrimmedMean.rho TrimmedMean.rho(z) [source] The robust criterion function for least trimmed mean. Parameters: z : array-like 1d array Returns: rho : array rho(z) = (1/2.)*z**2 for |z| <= c rho(z) = 0 for |z| > c

statsmodels.sandbox.distributions.transformed.Transf_gen.var Transf_gen.var(*args, **kwds) Variance of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution