statsmodels.tsa.arima.model.ARIMAResults¶

class statsmodels.tsa.arima.model.ARIMAResults(model, params, filter_results, cov_type=None, **kwargs)[source]

Class to hold results from fitting an SARIMAX model.

Parameters
modelSARIMAX instance

The fitted model instance

Attributes
specificationdictionary

Dictionary including all attributes from the SARIMAX model instance.

polynomial_arndarray

Array containing autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).

polynomial_mandarray

Array containing moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).

polynomial_seasonal_arndarray

Array containing seasonal autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).

polynomial_seasonal_mandarray

Array containing seasonal moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).

polynomial_trendndarray

Array containing trend polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).

model_orderslist of int

The orders of each of the polynomials in the model.

param_termslist of str

List of parameters actually included in the model, in sorted order.

Methods

 append(endog[, exog, refit, fit_kwargs]) Recreate the results object with new data appended to the original data apply(endog[, exog, refit, fit_kwargs]) Apply the fitted parameters to new data unrelated to the original data conf_int([alpha, cols]) Construct confidence interval for the fitted parameters. cov_params([r_matrix, column, scale, cov_p, …]) Compute the variance/covariance matrix. extend(endog[, exog]) Recreate the results object for new data that extends the original data f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. forecast([steps]) Out-of-sample forecasts get_forecast([steps]) Out-of-sample forecasts get_prediction([start, end, dynamic, index, …]) In-sample prediction and out-of-sample forecasting impulse_responses([steps, impulse, …]) Impulse response function info_criteria(criteria[, method]) Information criteria initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. load(fname) Load a pickled results instance See specific model class docstring plot_diagnostics([variable, lags, fig, figsize]) Diagnostic plots for standardized residuals of one endogenous variable predict([start, end, dynamic]) In-sample prediction and out-of-sample forecasting Remove data arrays, all nobs arrays from result and model. save(fname[, remove_data]) Save a pickle of this instance. simulate(nsimulations[, measurement_shocks, …]) Simulate a new time series following the state space model summary([alpha, start]) Summarize the Model 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. test_heteroskedasticity(method[, …]) Test for heteroskedasticity of standardized residuals test_normality(method) Test for normality of standardized residuals. test_serial_correlation(method[, lags]) Ljung-Box test for no serial correlation of standardized residuals 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 (float) Akaike Information Criterion aicc (float) Akaike Information Criterion with small sample correction arfreq (array) Frequency of the roots of the reduced form autoregressive lag polynomial arparams (array) Autoregressive parameters actually estimated in the model. arroots (array) Roots of the reduced form autoregressive lag polynomial bic (float) Bayes Information Criterion bse The standard errors of the parameter estimates. cov_params_approx (array) The variance / covariance matrix. cov_params_oim (array) The variance / covariance matrix. cov_params_opg (array) The variance / covariance matrix. cov_params_robust (array) The QMLE variance / covariance matrix. cov_params_robust_approx (array) The QMLE variance / covariance matrix. cov_params_robust_oim (array) The QMLE variance / covariance matrix. fittedvalues (array) The predicted values of the model. hqic (float) Hannan-Quinn Information Criterion llf (float) The value of the log-likelihood function evaluated at params. llf_obs (float) The value of the log-likelihood function evaluated at params. loglikelihood_burn (float) The number of observations during which the likelihood is not evaluated. mae (float) Mean absolute error mafreq (array) Frequency of the roots of the reduced form moving average lag polynomial maparams (array) Moving average parameters actually estimated in the model. maroots (array) Roots of the reduced form moving average lag polynomial mse (float) Mean squared error pvalues (array) The p-values associated with the z-statistics of the coefficients. resid (array) The model residuals. seasonalarparams (array) Seasonal autoregressive parameters actually estimated in the model. seasonalmaparams (array) Seasonal moving average parameters actually estimated in the model. sse (float) Sum of squared errors states tvalues Return the t-statistic for a given parameter estimate. use_t Flag indicating to use the Student’s distribution in inference. zvalues (array) The z-statistics for the coefficients.