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: 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
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.
Find the prediction intervals using the fitted ExponentialSmoothingRegressor.
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.
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.