HyperTreeARForecaster

class HyperTreeARForecaster(p=2, hessian_method='analytic', n_hessian_probes=5, lgb_params=None, num_iterations=100, seed=123)[source]

Hypertree-AR forecaster, from the hypertrees-forecasting package.

Direct interface to hypertrees.models.HyperTreeAR [1].

Hyper-Trees use a gradient boosted tree (LightGBM) to learn the parameters of a classical time series model as functions of features, rather than forecasting the series directly. HyperTreeAR targets a time-varying AR(p) model: the tree predicts the AR coefficients directly from the features at each time point, and the AR recursion generates the forecast. Unlike HyperTreeNetAR, no neural network is involved.

The interfaced estimator is univariate and models a single series.

Parameters:

pint, optional (default=2): Maximum number of AR(p) lags.
hessian_methodstr, optional (default=”analytic”): Method for the Hessian diagonal, one of "exact", "analytic", or "gn". "analytic" uses closed-form gradients and Hessians, exploiting that the AR fit is linear in its parameters.
n_hessian_probesint, optional (default=5): Number of Hutchinson probes, only used when hessian_method="gn".
lgb_paramsdict, optional (default=None): LightGBM parameters. If None, {"learning_rate": 0.1} is used.
num_iterationsint, optional (default=100): Number of boosting rounds.
seedint, optional (default=123): Random seed for the interfaced estimator.

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.

References

[1]

Maerz, Alexander, and Kashif Rasul. “Forecasting with Hyper-Trees.” arXiv preprint arXiv:2405.07836 (2024).

Examples

>>> from sktime.forecasting.hypertrees import HyperTreeARForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> forecaster = HyperTreeARForecaster(p=2)
>>> forecaster.fit(y, fh=[1, 2, 3])
HyperTreeARForecaster(...)
>>> y_pred = forecaster.predict()

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.