statsmodels.tsa.arima_model.ARIMAResults

class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0)[source]
Attributes
tvalues

Methods

arfreq()

Returns the frequency of the AR roots.

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.

forecast([steps, exog, alpha])

Out-of-sample forecasts

initialize(model, params, **kwd)

Initialize (possibly re-initialize) a Results instance.

load(fname)

load a pickle, (class method)

mafreq()

Returns the frequency of the MA roots.

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

aic

arparams

arroots

bic

bse

fittedvalues

hqic

llf

maparams

maroots

pvalues

resid