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statsmodels.tsa.arima_model.ARIMAResults

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

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()
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
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.

Attributes

use_t bool(x) -> bool

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