statsmodels.tsa.arima_model.ARMAResults.wald_test ARMAResults.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 arr

statsmodels.tsa.arima_model.ARMAResults.t_test ARMAResults.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 in

statsmodels.tsa.arima_model.ARMAResults.summary2 ARMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') [source] 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,

statsmodels.tsa.arima_model.ARMAResults.summary ARMAResults.summary(alpha=0.05) [source] 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

statsmodels.tsa.arima_model.ARMAResults.save ARMAResults.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 t

statsmodels.tsa.arima_model.ARMAResults.remove_data ARMAResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is access

statsmodels.tsa.arima_model.ARMAResults.plot_predict ARMAResults.plot_predict(start=None, end=None, exog=None, dynamic=False, alpha=0.05, plot_insample=True, ax=None) [source] Plot forecasts Parameters: 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 end forecasting, ie., the first for

statsmodels.tsa.arima_model.ARMAResults.predict ARMAResults.predict(start=None, end=None, exog=None, dynamic=False) [source] ARMA model in-sample and out-of-sample prediction Parameters: 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 end forecasting, ie., the first forecast is start.

statsmodels.tsa.arima_model.ARMAResults.load classmethod ARMAResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tsa.arima_model.ARMAResults.normalized_cov_params ARMAResults.normalized_cov_params()