PyKANForecaster

PyKANForecaster

class PyKANForecaster(hidden_layers=(1, 1), input_layer_size=2, grids=None, model_params=None, fit_params=None, val_size=0.5, device='cpu')[source]

PyKANForecaster uses Kolmogorov Arnold Network [1] to forecast time series data.

This forecaster uses the pykan library to create a KAN model to forecast time series data. The model is trained on the training data and then used to forecast the future values of the time series. Note This forecaster is experimental and the used library for implementing the KANs may be exchanged in the future to a more stable and efficient library.

Parameters:

hidden_layerstuple, optional (default=(1, 1))

The number of hidden layers in the network.

input_layer_sizeint, optional (default=2)

The size of the input layer.

kint, optional (default=3)

The number of nearest neighbors to consider.

gridsnp.array, optional (default=np.array([2, 3]))

The grid sizes to use in the model.

model_paramsdict, optional (default={“k”: 2})

The parameters to pass to the model. See pykan documentation for more details.

fit_paramsdict, optional (default={“steps”: 1})

The parameters to pass to the fit method. See pykan documentation for more details.

val_sizefloat, optional (default=0.5)

The size of the validation set to use in the training.

devicestr, optional (default=”cpu”)

The device to use for training 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.

References

[1]

Liu, Ziming, et al. “KAN: Kolmogorov-Arnold Networks.” arXiv preprint arXiv:2404.19756 (2024).

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.