HurstExponentTransformer

class HurstExponentTransformer(lags: list[int] | range | None = None, method: str = 'rs', min_lag: int = 2, max_lag: int = 100, fit_trend: str = 'c', confidence_level: float = 0.95)[source]

Transformer for calculating the Hurst exponent of a time series.

This transformer calculates the Hurst exponent, which is used to evaluate the auto-correlation properties of time series, particularly the degree of long-range dependence.

Parameters:

lagsOptional[Union[List[int], range]], default=None: The lags to use for calculation. If None, uses a range based on min_lag and max_lag.
methodstr, default=’rs’: The method to use for Hurst exponent calculation. Either ‘rs’ (rescaled range) or ‘dfa’ (detrended fluctuation analysis).
min_lagint, default=2: The minimum lag to use if lags is None.
max_lagint, default=100: The maximum lag to use if lags is None.
fit_trendstr, default=’c’: The trend component to include in the calculation.
confidence_levelfloat, default=0.95: The confidence level for the confidence interval calculation.

Attributes:

hurst_estimate_float: The estimated Hurst exponent.
confidence_interval_tuple: The confidence interval for the Hurst exponent estimate.

Examples

>>> from sktime.transformations.hurst import HurstExponentTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = HurstExponentTransformer()
>>> y_transform = transformer.fit_transform(y)

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`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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_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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`plot_log_log`(ts)	Plot the log-log graph used in Hurst exponent calculation.
`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.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.