statsmodels.tsa.holtwinters.Holt.fit¶
-
Holt.
fit
(smoothing_level=None, smoothing_slope=None, damping_slope=None, optimized=True, 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.
- 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
- 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’s exponential smoothing as per [1].
References
- [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
and practice. OTexts, 2014.