ExponentialSmoothingWrapper

class hcrystalball.wrappers.ExponentialSmoothingWrapper(endog=None, trend=None, damped_trend=False, seasonal=None, seasonal_periods=None, initialization_method=None, initial_level=None, initial_trend=None, initial_seasonal=None, use_boxcox=None, bounds=None, dates=None, freq=None, missing='none', name='ExponentialSmoothing', fit_params=None, clip_predictions_lower=None, clip_predictions_upper=None, hcb_verbose=True)[source]

Bases: hcrystalball.wrappers._statsmodels.BaseStatsmodelsForecastingWrapper

Wrapper for ExponentialSmoothing (see other parameters there)

Parameters
  • name (str) – Name of the model instance, used also as column name for returned prediction

  • fit_params (dict) – Parameters passed to fit method of model. For more details see statsmodels.tsa.holtwinters.ExponentialSmoothing.fit

  • clip_predictions_lower (float) – Minimal value allowed for predictions - predictions will be clipped to this value.

  • clip_predictions_upper (float) – Maximum value allowed for predictions - predictions will be clipped to this value.

  • hcb_verbose (bool) – Whtether to keep (True) or suppress (False) messages to stdout and stderr from the wrapper and 3rd party libraries during fit and predict

Methods Summary

fit(X, y)

Transform data to statsmodels.tsa.api required format and fit the model.

get_params([deep])

Get parameters for this estimator.

predict(X)

Transform data to statsmodels.tsa.api required format and provide predictions.

set_params(**params)

Set the parameters of this estimator.

Methods Documentation

fit(X, y)

Transform data to statsmodels.tsa.api required format and fit the model.

Parameters
  • X (pandas.DataFrame) – Input features.

  • y (array_like, (1d)) – Target vector.

Returns

Fitted model

Return type

self

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(X)

Transform data to statsmodels.tsa.api required format and provide predictions.

Parameters

X (pandas.DataFrame) – Input features.

Returns

Prediction stored in column with name being the name of the wrapper.

Return type

pandas.DataFrame

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance