statsmodels.tsa.arima_model.ARIMAResults

class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0)

Attributes

use_t

Flag indicating to use the Student’s distribution in inference.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
forecast([steps, exog, alpha])	Out-of-sample forecasts
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
plot_predict([start, end, exog, dynamic, …])	Plot forecasts
predict([start, end, exog, typ, dynamic])	ARIMA model in-sample and out-of-sample prediction
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
summary([alpha])	Summarize the Model
summary2([title, alpha, float_format])	Experimental summary function for ARIMA Results
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q.
t_test_pairwise(term_name[, method, alpha, …])	Perform pairwise t_test with multiple testing corrected p-values.
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
forecast([steps, exog, alpha])	Out-of-sample forecasts
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
plot_predict([start, end, exog, dynamic, …])	Plot forecasts
predict([start, end, exog, typ, dynamic])	ARIMA model in-sample and out-of-sample prediction
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
summary([alpha])	Summarize the Model
summary2([title, alpha, float_format])	Experimental summary function for ARIMA Results
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q.
t_test_pairwise(term_name[, method, alpha, …])	Perform pairwise t_test with multiple testing corrected p-values.
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns.

Properties

aic
arfreq	Returns the frequency of the AR roots.
arparams
arroots
bic
bse
cov_params_default
fittedvalues
hqic
llf
mafreq	Returns the frequency of the MA roots.
maparams
maroots
pvalues	The two-tailed p values for the t-stats of the params.
resid
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.