statsmodels.tsa.holtwinters.HoltWintersResults

class statsmodels.tsa.holtwinters.HoltWintersResults(model, params, **kwargs)[source]

Holt Winter’s Exponential Smoothing Results

Parameters
modelExponentialSmoothing instance

The fitted model instance

paramsdict

All the parameters for the Exponential Smoothing model.

Attributes
params: dict

All the parameters for the Exponential Smoothing model.

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.

fittedfcast: ndarray

An array of both the fitted values and forecast values.

fittedvalues: ndarray

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

fcastvalues: ndarray

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

sse: float

The sum of squared errors

level: ndarray

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

slope: ndarray

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

season: ndarray

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

aic: float

The Akaike information criterion.

bic: float

The Bayesian information criterion.

aicc: float

AIC with a correction for finite sample sizes.

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.

optimized: bool

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

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