statsmodels.tsa.holtwinters.HoltWintersResults¶

class statsmodels.tsa.holtwinters.HoltWintersResults(model, params, sse, aic, aicc, bic, optimized, level, trend, season, params_formatted, resid, k, fittedvalues, fittedfcast, fcastvalues, mle_retvals=None)[source]¶

Results from fitting Exponential Smoothing models.

Parameters:¶

model : ExponentialSmoothing instance¶

The fitted model instance.

params : dict¶

All the parameters for the Exponential Smoothing model.

sse : float¶

The sum of squared errors.

aic : float¶

The Akaike information criterion.

aicc : float¶

AIC with a correction for finite sample sizes.

bic : float¶

The Bayesian information criterion.

optimized : bool¶

Flag indicating whether the model parameters were optimized to fit the data.

level : ndarray¶

An array of the levels values that make up the fitted values.

trend : ndarray¶

An array of the trend values that make up the fitted values.

season : ndarray¶

An array of the seasonal values that make up the fitted values.

params_formatted : pd.DataFrame¶

DataFrame containing all parameters, their short names and a flag indicating whether the parameter’s value was optimized to fit the data.

resid : ndarray¶

An array of the residuals of the fittedvalues and actual values.

k : int¶

The k parameter used to remove the bias in AIC, BIC etc.

fittedvalues : ndarray¶

An array of the fitted values. Fitted by the Exponential Smoothing model.

fittedfcast : ndarray¶

An array of both the fitted values and forecast values.

fcastvalues : ndarray¶

An array of the forecast values forecast by the Exponential Smoothing model.

mle_retvals : {None, scipy.optimize.optimize.OptimizeResult}¶

Optimization results if the parameters were optimized to fit the data.

Methods

forecast([steps])	Out-of-sample forecasts
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
predict([start, end])	In-sample prediction and out-of-sample forecasting
simulate(nsimulations[, anchor, ...])	Random simulations using the state space formulation.
summary()	Summarize the fitted Model

Properties

aic	The Akaike information criterion.
aicc	AIC with a correction for finite sample sizes.
bic	The Bayesian information criterion.
fcastvalues	An array of the forecast values
fittedfcast	An array of both the fitted values and forecast values.
fittedvalues	An array of the fitted values
k	The k parameter used to remove the bias in AIC, BIC etc.
level	An array of the levels values that make up the fitted values.
mle_retvals	Optimization results if the parameters were optimized to fit the data.
model	The model used to produce the results instance.
optimized	Flag indicating if model parameters were optimized to fit the data.
params_formatted	DataFrame containing all parameters
resid	An array of the residuals of the fittedvalues and actual values.
season	An array of the seasonal values that make up the fitted values.
sse	The sum of squared errors between the data and the fittted value.
trend	An array of the trend values that make up the fitted values.