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

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 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.