statsmodels.tsa.holtwinters.Holt¶
-
class
statsmodels.tsa.holtwinters.
Holt
(endog, exponential=False, damped=False)[source]¶ Holt’s Exponential Smoothing
Parameters: endog : array_like
Time series
exponential : bool, optional
Type of trend component.
damped : bool, optional
Should the trend component be damped.
Returns: results : Holt class
See also
Notes
This is a full implementation of the Holt’s exponential smoothing as per [R108]. Holt is a restricted version of
ExponentialSmoothing
.References
[R108] (1, 2) Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014. Attributes
endog_names
Names of endogenous variables. exog_names
The names of the exogenous variables. Methods
fit
([smoothing_level, smoothing_slope, …])Fit the model from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe. hessian
(params)The Hessian matrix of the model. information
(params)Fisher information matrix of model. initial_values
()Compute initial values used in the exponential smoothing recursions initialize
()Initialize (possibly re-initialize) a Model instance. loglike
(params)Log-likelihood of model. predict
(params[, start, end])Returns in-sample and out-of-sample prediction. score
(params)Score vector of model. Methods
fit
([smoothing_level, smoothing_slope, …])Fit the model from_formula
(formula, data[, subset, drop_cols])Create a Model from a formula and dataframe. hessian
(params)The Hessian matrix of the model. information
(params)Fisher information matrix of model. initial_values
()Compute initial values used in the exponential smoothing recursions initialize
()Initialize (possibly re-initialize) a Model instance. loglike
(params)Log-likelihood of model. predict
(params[, start, end])Returns in-sample and out-of-sample prediction. score
(params)Score vector of model. Properties
endog_names
Names of endogenous variables. exog_names
The names of the exogenous variables.