Prophetverse

class Prophetverse(trend='linear', exogenous_effects=None, default_effect=None, feature_transformer=None, noise_scale=None, likelihood='normal', scale=None, rng_key=None, inference_engine=None, broadcast_mode='estimator')[source]

Univariate prophetverse forecaster - prophet model implemented in numpyro.

Estimator from the prophetverse package by felipeangelimvieira.

Differences to facebook’s prophet:

logistic trend. Here, another parametrization is considered, and the capacity is not passed as input, but inferred from the data.
the users can pass arbitrary sktime transformers as feature_transformer, for instance FourierFeatures or HolidayFeatures.
no default weekly_seasonality/yearly_seasonality, this is left to the user via the feature_transformer parameter
Uses changepoint_interval instead of n_changepoints to set

changepoints.

accepts configurations where each exogenous variable has a different function relating it to its additive effect on the time series. One can, for example, set different priors for a group of feature, or use a Hill function to model the effect of a feature.

Parameters:

trendUnion[str, BaseEffect], optional

Type of trend to use. Either “linear” (default) or “logistic”, or a custom effect object.

exogenous_effectsOptional[List[BaseEffect]], optional

List of effect objects defining the exogenous effects.

default_effectOptional[BaseEffect], optional

The default effect for variables without a specified effect.

feature_transformersktime transformer, optional

Transformer object to generate additional features (e.g.,: Fourier terms).

noise_scalefloat, optional

Scale parameter for the observation noise. Must be greater than 0. (default: 0.05)

likelihoodstr, optional

The likelihood model to use. One of “normal”, “gamma”, or: “negbinomial”. (default: “normal”)

scaleoptional

Scaling value inferred from the data.

rng_keyoptional

A jax.random.PRNGKey instance, or None.

inference_engineoptional

An inference engine for running the model.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.prophetverse import Prophetverse
>>> from prophetverse.effects.fourier import LinearFourierSeasonality
>>> from prophetverse.utils.regex import no_input_columns
>>> y = load_airline()
>>> model = Prophetverse(
...     exogenous_effects=[
...         (
...             "seasonality",
...             LinearFourierSeasonality(
...                 sp_list=[12],
...                 fourier_terms_list=[3],
...                 freq="M",
...                 effect_mode="multiplicative",
...             ),
...             no_input_columns,
...         )
...     ],
... )
>>> model.fit(y)
>>> model.predict(fh=[1, 2, 3])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the skbase object.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.