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: |
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Returns: |
results |
Return type: |
ExponentialSmoothing class |
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.
[1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.
fit ([smoothing_level, smoothing_slope, …]) | fit Holt Winter’s Exponential Smoothing |
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 |
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. |
endog_names | Names of endogenous variables |
exog_names |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html