statsmodels.tsa.arima_model.ARIMAResults

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

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()	Returns the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()
forecast([steps, exog, alpha])	Out-of-sample forecasts
hqic()
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
mafreq()	Returns the frequency of the MA roots.
maparams()
maroots()
normalized_cov_params()
plot_predict([start, end, exog, dynamic, …])	Plot forecasts
predict([start, end, exog, typ, dynamic])	ARIMA model in-sample and out-of-sample prediction
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
resid()
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
tvalues()	Return the t-statistic for a given parameter estimate.
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

Attributes

use_t