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

The Akaike Information Criterion

aicc

The Akaike Information Criterion with small sample correction

bic

The Bayes Information Criterion

bse

The standard errors of the parameter estimates

cov_params_approx

The variance / covariance matrix, computed using the numerical Hessian approximated by complex step or finite differences methods.

df_resid

The residual degrees of freedom

fittedvalues

hqic

The Hannan-Quinn Information Criterion

llf

Log-likelihood function evaluated at the fitted params

mae

The mean absolute error

mse

The mean squared error

nobs_effective

pvalues

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

resid

sse

The 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

The z-statistics for the coefficients

Methods

conf_int([alpha])	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
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, use_t])	Compute a t-test for 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

Methods

conf_int([alpha])	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
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, use_t])	Compute a t-test for 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	The Akaike Information Criterion
aicc	The Akaike Information Criterion with small sample correction
bic	The Bayes Information Criterion
bse	The standard errors of the parameter estimates
cov_params_approx	The variance / covariance matrix, computed using the numerical Hessian approximated by complex step or finite differences methods.
df_resid	The residual degrees of freedom
fittedvalues
hqic	The Hannan-Quinn Information Criterion
llf	Log-likelihood function evaluated at the fitted params
mae	The mean absolute error
mse	The mean squared error
nobs_effective
pvalues	The p-values associated with the z-statistics of the coefficients.
resid
sse	The 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	The z-statistics for the coefficients