NeuralProphet

class NeuralProphet(freq=None, add_seasonality=None, custom_seasonalities=None, add_country_holidays=None, growth='linear', changepoints=None, n_changepoints=10, changepoints_range=0.8, yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=False, seasonality_mode='additive', seasonality_reg=0, holidays=None, holidays_mode='additive', holidays_reg=0, trend_reg=0, trend_reg_threshold=False, learning_rate=None, epochs=None, batch_size=None, loss_func='Huber', alpha=0.05, uncertainty_samples=1000, verbose=False)[source]

NeuralProphet forecaster by wrapping NeuralProphet algorithm [1].

Direct interface to NeuralProphet, using the sktime interface. All hyper-parameters are exposed via the constructor.

Data can be passed in one of the sktime compatible formats. Like Prophet, NeuralProphet also supports integer/range and period index: * integer/range index is interpreted as days since Jan 1, 2000 * PeriodIndex is converted using the pandas method to_timestamp

Parameters:

freqstr, optional: Frequency of time series (e.g. ‘D’, ‘M’, etc.)
add_seasonalitydict, optional: Additional seasonality component parameters
custom_seasonalitieslist of dict, optional: Custom seasonality components
add_country_holidaysdict, optional: Country holidays to include
growthstr, default=”linear”: Type of trend (‘linear’ or ‘flat’)
changepointslist, optional: List of dates for trend changepoints
n_changepointsint, default=10: Number of potential trend changepoints
changepoints_rangefloat, default=0.8: Proportion of history for changepoints
yearly_seasonalitybool, default=True: Whether to include yearly seasonality
weekly_seasonalitybool, default=True: Whether to include weekly seasonality
daily_seasonalitybool, default=False: Whether to include daily seasonality
seasonality_modestr, default=”additive”: How seasonality is combined (‘additive’ or ‘multiplicative’)
seasonality_regfloat, default=0: Regularization strength for seasonality
holidayspd.DataFrame, optional: Custom holidays DataFrame
holidays_modestr, default=”additive”: How holidays are combined (‘additive’ or ‘multiplicative’)
holidays_regfloat, default=0: Regularization strength for holidays
trend_regfloat, default=0: Regularization strength for trend
trend_reg_thresholdbool, default=False: Threshold for trend regularization
learning_ratefloat, default=None: Maximum learning rate (applicable in quasi-Newton optimization)
epochsint, default=None: Number of training epochs
batch_sizeint, default=None: Number of samples per mini-batch
loss_funcstr, default=”Huber”: Type of loss to use (e.g., “Huber”, “MSE”, “MAE”, etc.)
alphafloat, default=0.05: Width of the uncertainty intervals
uncertainty_samplesint, default=1000: Number of samples for estimating uncertainty intervals
verbosebool, default=False: Whether to print status information during fitting

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.

Notes

NeuralProphet does not yet support pandas 3. Until upstream compatibility is restored, this interface requires pandas 2.

References

[1]

https://github.com/ourownstory/neural_prophet

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.neuralprophet import NeuralProphet
>>> # NeuralProphet requires data with a pandas.DatetimeIndex
>>> y = load_airline().to_timestamp(freq='M')
>>> forecaster = NeuralProphet(
...     n_changepoints=0,
...     yearly_seasonality=False,
...     weekly_seasonality=False,
...     daily_seasonality=False,
...     epochs=5,
...     uncertainty_samples=0,
...     verbose=False
... )
>>> forecaster.fit(y)
NeuralProphet(...)
>>> y_pred = forecaster.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 estimator.
`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.