HolidayFeatures

HolidayFeatures

class HolidayFeatures(calendar: dict[date, str], holiday_windows: dict[str, tuple] = None, include_bridge_days: bool = False, include_weekend: bool = False, return_dummies: bool = True, return_categorical: bool = False, return_indicator: bool = False, keep_original_columns: bool = False)[source]

Holiday features extraction.

HolidayFeatures uses a dictionary of holidays (which could be a custom made dict or imported as HolidayBase object from holidays package) to extract holiday features from a datetime index.

Parameters:

calendarHolidayBase object or Dict[date, str]

Calendar object from holidays package [1].

holiday_windowsDict[str, tuple], default=None

Dictionary for specifying a window of days around holidays, with keys being holiday names and values being (n_days_before, n_days_after) tuples.

include_bridge_days: bool, default=False

If True, include bridge days. Bridge days include Monday if a holiday is on Tuesday and Friday if a holiday is on Thursday.

include_weekend: bool, default=False

If True, include weekends as holidays.

return_dummiesbool, default=True

Whether or not to return a dummy variable for each holiday.

return_categoricalbool, default=False

Whether or not to return a categorical variable with holidays beings categories.

return_indicatorbool, default=False

Whether or not to return an indicator variable equal to 1 if a time point is a holiday or not.

keep_original_columnsbool, default=False

Keep original columns in X passed to .transform().

Attributes:

is_fitted

Whether fit has been called.

References

[1]

https://pypi.org/project/holidays/

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from datetime import date
>>> from holidays import country_holidays, financial_holidays
>>> values = np.random.normal(size=365)
>>> index = pd.date_range("2000-01-01", periods=365, freq="D")
>>> X = pd.DataFrame(values, index=index)

Returns country holiday features with custom holiday windows

>>> from sktime.transformations.holiday import HolidayFeatures
>>> transformer = HolidayFeatures(
...    calendar=country_holidays(country="FR"),
...    return_categorical=True,
...    holiday_windows={"Noël": (1, 3), "Jour de l'an": (1, 0)})
>>> yt = transformer.fit_transform(X)

Returns financial holiday features

>>> transformer = HolidayFeatures(
...    calendar=financial_holidays(market="NYSE"),
...    return_categorical=True,
...    include_weekend=True)
>>> yt = transformer.fit_transform(X)

Returns custom made holiday features

>>> transformer = HolidayFeatures(
...    calendar={date(2000,1,14): "Regional Holiday",
...              date(2000, 1, 26): "Regional Holiday"},
...    return_categorical=True)
>>> yt = transformer.fit_transform(X)

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.