statsmodels.tsa.holtwinters.ExponentialSmoothing.fit¶
-
ExponentialSmoothing.
fit
(smoothing_level=None, smoothing_slope=None, smoothing_seasonal=None, damping_slope=None, optimized=True, use_boxcox=False, remove_bias=False, use_basinhopping=False, start_params=None, initial_level=None, initial_slope=None, use_brute=True)[source]¶ Fit the model
- Parameters
- smoothing_level
float
,optional
The alpha value of the simple exponential smoothing, if the value is set then this value will be used as the value.
- smoothing_slope
float
,optional
The beta value of the Holt’s trend method, if the value is set then this value will be used as the value.
- smoothing_seasonal
float
,optional
The gamma value of the holt winters seasonal method, if the value is set then this value will be used as the value.
- damping_slope
float
,optional
The phi value of the damped method, if the value is set then this value will be used as the value.
- optimizedbool,
optional
Estimate model parameters by maximizing the log-likelihood
- use_boxcox{
True
,False
, ‘log’,float
},optional
Should the Box-Cox transform be applied to the data first? If ‘log’ then apply the log. If float then use lambda equal to float.
- remove_biasbool,
optional
Remove bias from forecast values and fitted values by enforcing that the average residual is equal to zero.
- use_basinhoppingbool,
optional
Using Basin Hopping optimizer to find optimal values
- start_params
ndarray
,optional
Starting values to used when optimizing the fit. If not provided, starting values are determined using a combination of grid search and reasonable values based on the initial values of the data
- initial_level
float
,optional
Value to use when initializing the fitted level.
- initial_slope
float
,optional
Value to use when initializing the fitted slope.
- use_brutebool,
optional
Search for good starting values using a brute force (grid) optimizer. If False, a naive set of starting values is used.
- smoothing_level
- Returns
- results
HoltWintersResults
class
See statsmodels.tsa.holtwinters.HoltWintersResults
- results
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.