statsmodels.tsa.holtwinters.HoltWintersResults¶
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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: 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 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 summary
()Summarize the fitted Model