statsmodels.tsa.holtwinters.ExponentialSmoothing¶
-
class
statsmodels.tsa.holtwinters.
ExponentialSmoothing
(endog, trend=None, damped=False, seasonal=None, seasonal_periods=None, dates=None, freq=None, missing='none')[source]¶ Holt Winter’s Exponential Smoothing
Parameters: endog : array_like
Time series
trend : {“add”, “mul”, “additive”, “multiplicative”, None}, optional
Type of trend component.
damped : bool, optional
Should the trend component be damped.
seasonal : {“add”, “mul”, “additive”, “multiplicative”, None}, optional
Type of seasonal component.
seasonal_periods : int, optional
The number of periods in a complete seasonal cycle, e.g., 4 for quarterly data or 7 for daily data with a weekly cycle.
Returns: results : ExponentialSmoothing class
Notes
This is a full implementation of the holt winters exponential smoothing as per [R107]. This includes all the unstable methods as well as the stable methods. The implementation of the library covers the functionality of the R library as much as possible whilst still being Pythonic.
References
[R107] (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.