BATSWrapper¶

class hcrystalball.wrappers.BATSWrapper(use_box_cox=None, box_cox_bounds=0, 1, use_trend=None, use_damped_trend=None, seasonal_periods=None, use_arma_errors=True, show_warnings=True, n_jobs=None, context=None, name='BATS', fit_params=None, conf_int=False, conf_int_level=0.95, clip_predictions_lower=None, clip_predictions_upper=None)[source]¶

Bases: hcrystalball.wrappers._tbats.BaseTBATSWrapper

Wrapper for BATS model

https://github.com/intive-DataScience/tbats

Brings BATS to sklearn time-series compatible interface and puts fit parameters to initialization stage.

Parameters

name (str) – Name of the model instance, used also as column name for returned prediction.
fit_params (dict) – Parameters passed to fit BATS model.
conf_int (bool) – Whether confidence intervals should be also outputed.
conf_int_level (float) – Confidence level of returned confidence interval
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.

Notes

Fitting the model might take significant time. You might consider advices from the author https://medium.com/p/cf3e4e80cf48/responses/show

Methods Summary

`fit`(X, y)	Fit the model.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Transform data to tbats required format and run the predictions.
`set_params`(**params)	Set the parameters of this estimator.

Methods Documentation

fit(X, y)¶

Fit the model.

Parameters

X (pandas.DataFrame) – Input features.
y (array_like, (1d)) – Target vector.

Returns

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 tbats required format and run the 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