HyperTreeNetARForecaster

class HyperTreeNetARForecaster(p=2, embedding_dimension=1, hidden_dim=128, dropout=0.1, use_random_projection=True, rp_embed_dim=12, network_learning_rate=0.001, gradient_mode='separate', device='cpu', hessian_method='exact', n_hessian_probes=5, lgb_params=None, num_iterations=100, seed=123)[source]

Hyper-TreeNet-AR forecaster, from the hypertrees-forecasting package.

Direct interface to hypertrees.models.HyperTreeNetAR [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. HyperTreeNetAR targets a time-varying AR(p) model: the tree produces embeddings that a small neural network maps to the AR parameters, and the AR model generates the forecast.

The interfaced estimator is univariate and models a single series.

Parameters:

pint, optional (default=2): Maximum number of AR(p) lags.
embedding_dimensionint, optional (default=1): Embedding dimension of the tree embeddings fed to the network.
hidden_dimint, optional (default=128): Hidden dimension of the embedding network (MLP).
dropoutfloat, optional (default=0.1): Dropout rate of the embedding network.
use_random_projectionbool, optional (default=True): Whether to use random projections for the embeddings.
rp_embed_dimint, optional (default=12): Dimension of the random projections, only used when use_random_projection=True.
network_learning_ratefloat, optional (default=1e-3): Learning rate of the embedding network optimizer.
gradient_modestr, optional (default=”separate”): Gradient computation mode, "separate" or "shared".
devicestr, optional (default=”cpu”): Device for the embedding network, e.g. "cpu" or "cuda".
hessian_methodstr, optional (default=”exact”): Method for the Hessian diagonal, "exact" or "gn".
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 HyperTreeNetARForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> forecaster = HyperTreeNetARForecaster(p=2)
>>> forecaster.fit(y, fh=[1, 2, 3])
HyperTreeNetARForecaster(...)
>>> 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.