FinancialHolidaysTransformer

class FinancialHolidaysTransformer(market, years=None, expand=True, observed=True, name=None)[source]

Financial Holidays Transformer.

This implementation wraps over holidays [1] by vacanza.

Based on the index of X, dates are extracted and passed to holidays. Then upon generating the holiday information for that day (or absence of it) based on passed financial market information, a boolean series is prepared where True indicates the date being a holiday and False otherwise. fit is a no-op for this transformer.

Parameters:

marketstr: An ISO 3166-1 Alpha-2 market code [2]; not implemented for all countries (see documentation [3]).
yearsUnion[int, Iterable[int]], optional: The year(s) to pre-calculate public holidays for at instantiation.
expandbool, optional: Whether the entire year is calculated when one date from that year is requested.
observedbool, optional: Whether to include the dates of when public holiday are observed (e.g. a holiday falling on a Sunday being observed the following Monday). False may not work for all countries.
namestr, optional: name of transformed series.

Attributes:

is_fitted: Whether fit has been called.

Notes

If name is missing (by default), it is auto-populated as {market}_holidays.

References

[1]

https://github.com/vacanza/python-holidays

[2]

https://www.iso20022.org/market-identifier-codes

[3]

https://python-holidays.readthedocs.io/en/latest/#available-financial-markets

Examples

>>> from sktime.transformations.holiday import FinancialHolidaysTransformer
>>>
>>> import numpy
>>> data = numpy.random.default_rng(seed=0).random(size=365)
>>>
>>> import pandas
>>> index = pandas.date_range(start="2023-01-01", end="2023-12-31", freq="D")
>>>
>>> y = pandas.Series(data, index=index, name="random")
>>>
>>> y_t = FinancialHolidaysTransformer("XNYS").fit_transform(y)
>>> y_t.dtype
dtype('bool')
>>> y_t.sum()
10
>>> y_t.name
'XNYS_holidays'

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.
`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.