statsmodels.tsa.exponential_smoothing.ets.ETSResults¶

class statsmodels.tsa.exponential_smoothing.ets.ETSResults(model, params, results)[source]

Results from an error, trend, seasonal (ETS) exponential smoothing model

Attributes
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. Computed using the numerical Hessian approximated by complex step or finite differences methods.

df_resid
fittedvalues
hqic

(float) Hannan-Quinn Information Criterion

llf

log-likelihood function evaluated at the fitted params

mae

(float) Mean absolute error

mse

(float) Mean squared error

nobs_effective
pvalues

(array) The p-values associated with the z-statistics of the coefficients. Note that the coefficients are assumed to have a Normal distribution.

resid
sse

(float) Sum of squared errors

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.

Methods

 conf_int([alpha, cols]) Construct confidence interval for the fitted parameters. cov_params([r_matrix, column, scale, cov_p, ...]) Compute the variance/covariance matrix. f_test(r_matrix[, cov_p, invcov]) Compute the F-test for a joint linear hypothesis. forecast([steps]) Out-of-sample forecasts get_prediction([start, end, dynamic, index, ...]) Calculates mean prediction and prediction intervals. initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. load(fname) Load a pickled results instance See specific model class docstring predict([start, end, dynamic, index]) 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[, anchor, ...]) Random simulations using the state space formulation. summary([alpha, start]) Summarize the fitted model t_test(r_matrix[, cov_p, 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, invcov, use_f, ...]) 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. df_resid fittedvalues hqic (float) Hannan-Quinn Information Criterion llf log-likelihood function evaluated at the fitted params mae (float) Mean absolute error mse (float) Mean squared error nobs_effective pvalues (array) The p-values associated with the z-statistics of the coefficients. resid sse (float) Sum of squared errors 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.