statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults

class statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults(model, params, filter_results, cov_type=None, **kwargs)

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, fit_kwargs])	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
normalized_cov_params()	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()	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
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
mse	(float) Mean squared error
pvalues	(array) The p-values associated with the z-statistics of the coefficients.
resid	(array) The model residuals.
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.