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: 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