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
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 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.

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.