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
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.
`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.

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