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)

Results from fitting Exponential Smoothing models

Parameters:

modelExponentialSmoothing instance

The fitted model instance.

paramsdict

All the parameters for the Exponential Smoothing model.

ssefloat

The sum of squared errors.

aicfloat

The Akaike information criterion.

aiccfloat

AIC with a correction for finite sample sizes.

bicfloat

The Bayesian information criterion.

optimizedbool

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

levelndarray

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

trendndarray

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

seasonndarray

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

params_formattedpd.DataFrame

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

residndarray

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

kint

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

fittedvaluesndarray

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

fittedfcastndarray

An array of both the fitted values and forecast values.

fcastvaluesndarray

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.

Attributes:

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 level 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 fitted value

trend

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

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

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 level 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 fitted value
trend	An array of the trend values that make up the fitted values