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.