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
- endogarray_like
Time series
- trend{“add”, “mul”, “additive”, “multiplicative”,
None
},optional
Type of trend component.
- dampedbool,
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
- results
Notes
This is a full implementation of the holt winters exponential smoothing as per [R2ac3c82b3ff9-1]. 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
- R2ac3c82b3ff9-1
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 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
Names of endogenous variables.
The names of the exogenous variables.