time_series_baseline_estimator#

Time series estimator that predicts using the naive forecasting approach.

Module Contents#

Classes Summary#

TimeSeriesBaselineEstimator

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

`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 importance associated with each feature.
`fit`	Fits time series baseline estimator 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 time series baseline estimator.
`predict_proba`	Make prediction probabilities using fitted time series baseline estimator.
`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 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)

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.

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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