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#
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(time_index: Optional[Hashable] = None, changepoint_prior_scale: float = 0.05, seasonality_prior_scale: int = 10, holidays_prior_scale: int = 10, seasonality_mode: str = 'additive', stan_backend: str = 'CMDSTANPY', interval_width: float = 0.95, random_seed: Union[int, float] = 0, **kwargs)[source]#
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/
- Parameters
time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.
changepoint_prior_scale (float) – Determines the strength of the sparse prior for fitting on rate changes. Increasing this value increases the flexibility of the trend. Defaults to 0.05.
seasonality_prior_scale (int) – Similar to changepoint_prior_scale. Adjusts the extent to which the seasonality model will fit the data. Defaults to 10.
holidays_prior_scale (int) – Similar to changepoint_prior_scale. Adjusts the extent to which holidays will fit the data. Defaults to 10.
seasonality_mode (str) – Determines how this component fits the seasonality. Options are “additive” and “multiplicative”. Defaults to “additive”.
stan_backend (str) – Determines the backend that should be used to run Prophet. Options are “CMDSTANPY” and “PYSTAN”. Defaults to “CMDSTANPY”.
interval_width (float) – Determines the confidence of the prediction interval range when calling get_prediction_intervals. Accepts values in the range (0,1). Defaults to 0.95.
random_seed (int) – Seed for the random number generator. Defaults to 0.
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 the Prophet data to pass fit and predict on.
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 len(1) as feature_importance is not defined for Prophet regressor.
Fits Prophet regressor component to data.
Get parameters for the Prophet regressor.
Find the prediction intervals using the fitted ProphetRegressor.
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 Prophet regressor.
Make probability estimates for labels.
Saves component at file path.
Updates the parameter dictionary of the component.
- static build_prophet_df(X: pandas.DataFrame, y: Optional[pandas.Series] = None, time_index: str = 'ds') pandas.DataFrame [source]#
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) dict #
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) numpy.ndarray #
Returns array of 0’s with len(1) as feature_importance is not defined for Prophet regressor.
- fit(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None)[source]#
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_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 ProphetRegressor.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.Series) – Target data. Ignored.
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 Prophet estimator.
- 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 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: 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.