BKFilter

class BKFilter(low=6, high=32, K=12)[source]

Filter a times series using the Baxter-King filter.

This is a wrapper around the bkfilter function from statsmodels. (see statsmodels.tsa.filters.bk_filter.bkfilter).

The Baxter-King filter is intended for economic and econometric time series data and deals with the periodicity of the business cycle. Applying their band-pass filter to a series will produce a new series that does not contain fluctuations at a higher or lower frequency than those of the business cycle. Baxter-King follow Burns and Mitchell’s work on business cycles, which suggests that U.S. business cycles typically last from 1.5 to 8 years.

Parameters:

lowfloat: Minimum period for oscillations. Baxter and King recommend a value of 6 for quarterly data and 1.5 for annual data.
highfloat: Maximum period for oscillations. BK recommend 32 for U.S. business cycle quarterly data and 8 for annual data.
Kint: Lead-lag length of the filter. Baxter and King suggest a truncation length of 12 for quarterly data and 3 for annual data.

Attributes:

is_fitted: Whether fit has been called.

Notes

Returns a centered weighted moving average of the original series.

References

Baxter, M. and R. G. King. “Measuring Business Cycles: Approximate: Band-Pass Filters for Economic Time Series.” Review of Economics and Statistics, 1999, 81(4), 575-593.

Examples

>>> from sktime.transformations.bkfilter import BKFilter
>>> 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)
>>> bk = BKFilter(6, 24, 12)
>>> cycles = bk.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.