time_series_baseline_estimator¶
Time series estimator that predicts using the naive forecasting approach.
Module Contents¶
Classes Summary¶
Time series estimator that predicts using the naive forecasting approach. |
Contents¶
-
class
evalml.pipelines.components.estimators.regressors.time_series_baseline_estimator.
TimeSeriesBaselineEstimator
(gap=1, forecast_horizon=1, random_seed=0, **kwargs)[source]¶ Time series estimator that predicts using the naive forecasting approach.
This is useful as a simple baseline estimator for time series problems.
- Parameters
gap (int) – Gap between prediction date and target date and must be a positive integer. If gap is 0, target date will be shifted ahead by 1 time period. Defaults to 1.
forecast_horizon (int) – Number of time steps the model is expected to predict.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
model_family
ModelFamily.BASELINE
modifies_features
True
modifies_target
False
name
Time Series Baseline Estimator
supported_problem_types
[ ProblemTypes.TIME_SERIES_REGRESSION, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]
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 importance associated with each feature.
Fits time series baseline estimator 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 time series baseline estimator.
Make prediction probabilities using fitted time series baseline estimator.
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.
-
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 importance associated with each feature.
Since baseline estimators do not use input features to calculate predictions, returns an array of zeroes.
- Returns
An array of zeroes.
- Return type
np.ndarray (float)
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fit
(self, X, y=None)[source]¶ Fits time series baseline estimator to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If input y is None.
-
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)[source]¶ Make predictions using fitted time series baseline estimator.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
- Returns
Predicted values.
- Return type
pd.Series
- Raises
ValueError – If input y is None.
-
predict_proba
(self, X)[source]¶ Make prediction probabilities using fitted time series baseline estimator.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
- Returns
Predicted probability values.
- Return type
pd.DataFrame
- Raises
ValueError – If input y is None.
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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.