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

statsmodels.tsa.arima_model.ARMA.information ARMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.arima_model.ARMA.geterrors ARMA.geterrors(params) [source] Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.tsa.arima_model.ARMA.from_formula classmethod ARMA.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra argumen

statsmodels.tsa.arima_model.ARMA.fit ARMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to trans

statsmodels.tsa.arima_model.ARIMAResults.wald_test ARIMAResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of a

statsmodels.tsa.arima_model.ARIMAResults.t_test ARIMAResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays i

statsmodels.tsa.arima_model.ARIMAResults.summary2 ARIMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') Experimental summary function for ARIMA Results Parameters: title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string : print format for floats in parameters summary Returns: smry : Summary instance This holds the summary table and text, which

statsmodels.tsa.arima_model.ARIMAResults.save ARIMAResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data is

statsmodels.tsa.arima_model.ARIMAResults.summary ARIMAResults.summary(alpha=0.05) Summarize the Model Parameters: alpha : float, optional Significance level for the confidence intervals. Returns: smry : Summary instance This holds the summary table and text, which can be printed or converted to various output formats. See also statsmodels.iolib.summary.Summary