prophet_regressor

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Module Contents

Classes Summary

ProphetRegressor

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Contents

class evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor(date_index=None, changepoint_prior_scale=0.05, seasonality_prior_scale=10, holidays_prior_scale=10, seasonality_mode='additive', random_seed=0, stan_backend='CMDSTANPY', **kwargs)

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

More information here: https://facebook.github.io/prophet/

Attributes

hyperparameter_ranges	{ “changepoint_prior_scale”: Real(0.001, 0.5), “seasonality_prior_scale”: Real(0.01, 10), “holidays_prior_scale”: Real(0.01, 10), “seasonality_mode”: [“additive”, “multiplicative”],}
model_family	ModelFamily.PROPHET
modifies_features	True
modifies_target	False
name	Prophet Regressor
supported_problem_types	[ProblemTypes.TIME_SERIES_REGRESSION]
training_only	False

Methods

build_prophet_df	Build the Prophet data to pass fit and predict on.
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 array of 0’s with len(1) as feature_importance is not defined for Prophet regressor.
fit	Fits Prophet regressor component to data.
get_params	Get parameters for the Prophet regressor.
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 Prophet regressor.
predict_proba	Make probability estimates for labels.
save	Saves component at file path.

static build_prophet_df(X, y=None, date_index='ds')

Build the Prophet data to pass fit and predict on.

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.

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 array of 0’s with len(1) as feature_importance is not defined for Prophet regressor.

fit(self, X, y=None)

Fits Prophet regressor component 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

get_params(self)

Get parameters for the Prophet regressor.

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, y=None)

Make predictions using fitted Prophet regressor.

Parameters

• X (pd.DataFrame) – Data of shape [n_samples, n_features].

• y (pd.Series) – Target data. Ignored.

Returns

Predicted values.

Return type

pd.Series

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.

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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