HoltSmoothingWrapper¶

class hcrystalball.wrappers.HoltSmoothingWrapper(endog=None, exponential=False, damped_trend=False, initialization_method=None, initial_level=None, initial_trend=None, name='HoltSmoothing', fit_params=None, clip_predictions_lower=None, clip_predictions_upper=None)[source]¶

Bases: hcrystalball.wrappers._statsmodels.BaseStatsmodelsForecastingWrapper

Wrapper for Holt (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.Holt.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.

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: mapping of string to any

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 pipelines). 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: object