GreykiteForecaster

class GreykiteForecaster(forecast_config: GreykiteForecaster.ForecastConfig | None = None, date_format: str | None = None, model_template: str = 'SILVERKITE', coverage: float = 0.95)[source]

Adapter for using Greykite forecasting models within sktime.

This forecaster wraps Greykite forecast_pipeline (configured via a ForecastConfig) and exposes a sktime-compatible API.

WARNING: the greykite package has very restrictive dependencies that typically prevent installation together with other packages. For this reason, this estimator is also not covered by regular tests. We therefore recommend to run check_estimator(GreykiteForecaster) on your system before deploying this estimator.

Parameters:

forecast_configForecastConfig, optional: Configuration object for Greykite’s forecasting pipeline. If None, a default configuration is created.
date_formatstr, optional: Format of the timestamp in the data. If None, it is inferred.
model_templatestr, optional: Name of the model template to use (default: “SILVERKITE”).
coveragefloat, optional: Intended coverage of the prediction bands (0.0 to 1.0).

Attributes:

_forecasterobject: The fitted Greykite forecaster.
_forecastpandas.DataFrame: The forecast result from the Greykite model.
_Xpandas.DataFrame: The exogenous variables, if provided.

References

[1]

https://linkedin.github.io/greykite/docs/1.0.0/html/pages/stepbystep/0400_configuration.html

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.greykite import GreykiteForecaster
>>> from sktime.forecasting.base import ForecastingHorizon
>>> y = load_airline()
>>> fh = ForecastingHorizon([1, 2, 3])
>>> forecaster = GreykiteForecaster()
>>> forecaster.fit(y=y, fh=fh)
>>> y_pred = forecaster.predict(fh=fh)

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`()	Return 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 GreykiteForecaster.
`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.