exponential_smoothing_regressor#

Holt-Winters Exponential Smoothing Forecaster.

Module Contents#

Classes Summary#

ExponentialSmoothingRegressor

Holt-Winters Exponential Smoothing Forecaster.

Contents#

class evalml.pipelines.components.estimators.regressors.exponential_smoothing_regressor.ExponentialSmoothingRegressor(trend: Optional[str] = None, damped_trend: bool = False, seasonal: Optional[str] = None, sp: int = 2, n_jobs: int = - 1, random_seed: Union[int, float] = 0, **kwargs)[source]#

Holt-Winters Exponential Smoothing Forecaster.

Currently ExponentialSmoothingRegressor isn’t supported via conda install. It’s recommended that it be installed via PyPI.

Parameters

  • trend (str) – Type of trend component. Defaults to None.

  • damped_trend (bool) – If the trend component should be damped. Defaults to False.

  • seasonal (str) – Type of seasonal component. Takes one of {“additive”, None}. Can also be multiplicative if

  • 0 (none of the target data is) –

  • None. (but AutoMLSearch wiill not tune for this. Defaults to) –

  • sp (int) – The number of seasonal periods to consider. Defaults to 2.

  • n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. Defaults to -1.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges

{ “trend”: [None, “additive”], “damped_trend”: [True, False], “seasonal”: [None, “additive”], “sp”: Integer(2, 8),}

model_family

ModelFamily.EXPONENTIAL_SMOOTHING

modifies_features

True

modifies_target

False

name

Exponential Smoothing Regressor

supported_problem_types

[ProblemTypes.TIME_SERIES_REGRESSION]

training_only

False

Methods

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

feature_importance

Returns array of 0's with a length of 1 as feature_importance is not defined for Exponential Smoothing regressor.

fit

Fits Exponential Smoothing Regressor to data.

get_prediction_intervals

Find the prediction intervals using the fitted ExponentialSmoothingRegressor.

load

Loads component at file path.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

predict

Make predictions using fitted Exponential Smoothing regressor.

predict_proba

Make probability estimates for labels.

save

Saves component at file path.

update_parameters

Updates the parameter dictionary of the component.
clone(self)#

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters

  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

property feature_importance(self) → pandas.Series#

Returns array of 0’s with a length of 1 as feature_importance is not defined for Exponential Smoothing regressor.

fit(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None)[source]#

Fits Exponential Smoothing Regressor to data.

Parameters

  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.

  • y (pd.Series) – The target training data of length [n_samples].

Returns

self

Raises

ValueError – If y was not passed in.

get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) → Dict[str, pandas.Series][source]#

Find the prediction intervals using the fitted ExponentialSmoothingRegressor.

Calculates the prediction intervals by using a simulation of the time series following a specified state space model.

Parameters

  • X (pd.DataFrame) – Data of shape [n_samples, n_features].

  • y (pd.Series) – Target data. Optional.

  • coverage (List[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.

  • predictions (pd.Series) – Not used for Exponential Smoothing regressor.

Returns

Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.

Return type

dict

static load(file_path)#

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)#

Returns the parameters which were used to initialize the component.

predict(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None) → pandas.Series[source]#

Make predictions using fitted Exponential Smoothing regressor.

Parameters

  • X (pd.DataFrame) – Data of shape [n_samples, n_features]. Ignored except to set forecast horizon.

  • y (pd.Series) – Target data.

Returns

Predicted values.

Return type

pd.Series

predict_proba(self, X: pandas.DataFrame) → pandas.Series#

Make probability estimates for labels.

Parameters

X (pd.DataFrame) – Features.

Returns

Probability estimates.

Return type

pd.Series

Raises

MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters

  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

  • update_dict (dict) – A dict of parameters to update.

  • reset_fit (bool, optional) – If True, will set _is_fitted to False.