CFFilter

class CFFilter(low=6, high=32, drift=True)[source]

Filter a times series using the Christiano Fitzgerald filter.

This is a wrapper around the cffilter function from statsmodels. (see statsmodels.tsa.filters.cf_filter.cffilter).

Parameters:

lowfloat, optional, default = 6.0: Minimum period of oscillations. Features below low periodicity are filtered out. For quarterly data, the default of 6 gives 1.5 years periodicity.
highfloat, optional, default = 32.0: Maximum period of oscillations. Features above high periodicity are filtered out. For quarterly data, the default of 32 gives 8 year periodicity.
driftbool, optional, default = True: Whether or not to subtract a trend from the data. The trend is estimated as np.arange(nobs)*(x[-1] -x[0])/(len(x)-1). > X : argument of CFFilter._transform() > x : If X is 1d, X=x. If 2d, x is assumed to be in columns. > nobs : len(x)

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.cffilter import CFFilter
>>> import pandas as pd
>>> import statsmodels.api as sm
>>> dta = sm.datasets.macrodata.load_pandas().data
>>> index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q')
>>> dta.set_index(index, inplace=True)
>>> cf = CFFilter(6, 24, True)
>>> cycles = cf.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.