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statsmodels.tsa.holtwinters.Holt

class statsmodels.tsa.holtwinters.Holt(endog, exponential=False, damped=False) [source]

Holt’s Exponential Smoothing wrapper(…)

Parameters:
  • endog (array-like) – Time series
  • expoential (bool, optional) – Type of trend component.
  • damped (bool, optional) – Should the trend component be damped.
Returns:

results

Return type:

Holt class

Notes

This is a full implementation of the holts exponential smoothing as per [1].

See also

Exponential, Simple

References

[1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

Methods

fit([smoothing_level, smoothing_slope, …]) fit Holt’s Exponential Smoothing wrapper(…)
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.

Attributes

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.Holt.html