exponential_smoothing_regressor¶
Holt-Winters Exponential Smoothing Forecaster.
Module Contents¶
Classes Summary¶
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Returns array of 0’s with a length of 1 as feature_importance is not defined for Exponential Smoothing regressor.
Fits Exponential Smoothing Regressor to data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Make predictions using fitted Exponential Smoothing regressor.
Make probability estimates for labels.
Saves component at file path.
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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.
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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
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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
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property
feature_importance
(self)¶ Returns array of 0’s with a length of 1 as feature_importance is not defined for Exponential Smoothing regressor.
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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.
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static
load
(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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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.
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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
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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.
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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.