HPFilter

HPFilter

class HPFilter(lamb=1600)[source]

Filter a times series using the Hodrick-Prescott filter.

This is a wrapper around the hpfilter function from statsmodels. (see statsmodels.tsa.filters.hp_filter.hpfilter).

Parameters:

lambfloat

The Hodrick-Prescott smoothing parameter. A value of 1600 is suggested for quarterly data. Ravn and Uhlig suggest using a value of 6.25 (1600/4**4) for annual data and 129600 (1600*3**4) for monthly data.

Attributes:

is_fitted

Whether fit has been called.

Notes

The HP filter removes a smooth trend

Hodrick, R.J, and E. C. Prescott. 1980. “Postwar U.S. Business Cycles: An

Empirical Investigation.” Carnegie Mellon University discussion paper no. 451.

Ravn, M.O and H. Uhlig. 2002. “Notes On Adjusted the Hodrick-Prescott

Filter for the Frequency of Observations.” The Review of Economics and Statistics, 84(2), 371-80.

Examples

>>> from sktime.transformations.hpfilter import HPFilter
>>> import pandas as pd
>>> import statsmodels.api as sm
>>> dta = sm.datasets.macrodata.load_pandas().data
>>> index = pd.period_range('1959Q1', '2009Q3', freq='Q')
>>> dta.set_index(index, inplace=True)
>>> hp = HPFilter(1600)
>>> cycles = hp.fit_transform(X=dta[['realinv']])

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.