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 (arraylike) – 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 seasons to consider for the holt winters.
 Returns
results
 Return type
ExponentialSmoothing class
Notes
This is a full implementation of the holt winters exponential smoothing as per [1]. 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
 [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
and practice. OTexts, 2014.
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
Compute initial values used in the exponential smoothing recursions
Initialize (possibly reinitialize) a Model instance.
loglike
(params)Loglikelihood of model.
predict
(params[, start, end])Returns insample and outofsample prediction.
score
(params)Score vector of model.
Attributes
endog_names
Names of endogenous variables
exog_names