statsmodels.tsa.holtwinters.HoltWintersResults

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

Holt Winter’s Exponential Smoothing Results

Parameters:

model : ExponentialSmoothing instance

The fitted model instance

params : dict

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: array An array of both the fitted values and forecast values.
fittedvalues: array An array of the fitted values. Fitted by the Exponential Smoothing model.
fcastvalues: array An array of the forecast values forecast by the Exponential Smoothing model.
sse: float The sum of squared errors
level: array An array of the levels values that make up the fitted values.
slope: array An array of the slope values that make up the fitted values.
season: array 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: array 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, **kwd) Initialize (possibly re-initialize) a Results instance.
predict([start, end]) In-sample prediction and out-of-sample forecasting
summary() Summarize the fitted Model