statsmodels.tsa.holtwinters.HoltWintersResults¶
-
class
statsmodels.tsa.holtwinters.HoltWintersResults(model, params, **kwargs)[source]¶ Holt Winter’s Exponential Smoothing Results
- Parameters
- model
ExponentialSmoothinginstance The fitted model instance
- params
dict All the parameters for the Exponential Smoothing model.
- 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, **kwargs)Initialize (possibly re-initialize) a Results instance.
predict([start, end])In-sample prediction and out-of-sample forecasting
summary()Summarize the fitted Model