ProphetHyperparams

prophet_model.ProphetHyperparams

This class defines the hyperparameters used to configure the underlying Prophet model.

__init__(self, **data)

Initializes the hyperparameter configuration and enforces that only one seasonality option (yearly_seasonality, weekly_seasonality, or daily_seasonality) can be enabled simultaneously.

Parameter	Type	Default	Description
growth	Literal["linear", "logistic", "flat"]	"linear"	Growth type
seasonality_mode	Literal["additive", "multiplicative"]	-	Seasonality mode
changepoint_prior_scale	float	-	Changepoint prior scale
seasonality_prior_scale	float	-	Seasonality prior scale
yearly_seasonality	Optional[Union[int, bool]]	False	Enable yearly seasonality (bool) or increase the number of Fourier terms (int)
weekly_seasonality	Optional[Union[int, bool]]	False	Enable weekly seasonality (bool) or increase the number of Fourier terms (int)
daily_seasonality	Optional[Union[int, bool]]	False	Enable daily seasonality (bool) or increase the number of Fourier terms (int)

ProphetModel

prophet_model.ProphetModel

__init__(self, params: Dict[str, Any], settings: Dict[str, Any])

Initializes the ProphetModel, setting up hyperparameter validation using ProphetHyperparams.

Parameter	Type	Default	Description
params	Dict[str, Any]	-	(undocumented)
settings	Dict[str, Any]	-	(undocumented)

train(self, y_context: np.ndarray, y_target: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs: dict) -> ProphetModel

Trains a separate Prophet model for each variate in a (potentially multivariate) time series.

Expects y_context and y_target as 2D arrays: (num_steps, num_targets).

Parameters:

Parameter	Type	Default	Description
y_context	np.ndarray	-	Context values, shape (num_steps, num_targets).
y_target	np.ndarray	-	Context values, shape (num_steps, num_targets).
timestamps_context	np.ndarray	-	(undocumented)
timestamps_target	np.ndarray	-	(undocumented)
x_context	Optional[np.ndarray]	None	(undocumented)
x_target	Optional[np.ndarray]	None	(undocumented)
**kwargs	dict	-	Additional keyword arguments.

Returns: ProphetModel (the fitted forecaster).

predict(self, y_context: np.ndarray, timestamps_context: np.ndarray, timestamps_target: np.ndarray, x_context: Optional[np.ndarray] = None, x_target: Optional[np.ndarray] = None, **kwargs: dict) -> np.ndarray

Predicts future values for each variate and concatenates the results.

Parameters:

Parameter	Type	Default	Description
y_context	np.ndarray	-	Context values, shape (num_steps, num_variates).
timestamps_context	np.ndarray	-	Timestamps for context data.
timestamps_target	np.ndarray	-	Timestamps for target/future data.
x_context	Optional[np.ndarray]	None	Optional covariate data for context.
x_target	Optional[np.ndarray]	None	Optional covariate data for prediction horizon.
**kwargs	dict	-	Additional keyword arguments.

Returns: np.ndarray (Predictions with shape (forecast_horizon, num_variates)). Raises: ValueError (If the model has not been fitted.)