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
(float) Akaike Information Criterion
(float) Akaike Information Criterion with small sample correction
(float) Bayes Information Criterion
The standard errors of the parameter estimates.
(array) The variance / covariance matrix.
(float) Hannan-Quinn Information Criterion
log-likelihood function evaluated at the fitted params
(float) Mean absolute error
(float) Mean squared error
(array) The p-values associated with the z-statistics of the coefficients.
(float) Sum of squared errors
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student's distribution in inference.
(array) The z-statistics for the coefficients.