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=None, damped_trend=False, seasonal=None, sp=2, n_jobs=- 1, random_seed=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

  • of the target data is 0 (none) –

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

  • 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.

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.

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)

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

fit(self, X, y=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.

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, y=None)[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)

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.