statsmodels.tsa.exponential_smoothing.ets.ETSResults

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

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, scale, 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
normalized_cov_params()	See specific model class docstring
predict([start, end, dynamic, index])	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[, anchor, …])	Random simulations using the state space formulation.
summary([alpha, start])	Summarize the fitted 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.

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, scale, 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
normalized_cov_params()	See specific model class docstring
predict([start, end, dynamic, index])	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[, anchor, …])	Random simulations using the state space formulation.
summary([alpha, start])	Summarize the fitted 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.
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.