SavitzkyGolayTransformer

class SavitzkyGolayTransformer(window_length=5, polyorder=2, deriv=0, delta=1.0, mode='interp', cval=0.0)[source]

Savitzky-Golay filter for smoothing or differentiating time series.

Uses local polynomial regression (convolution) to smooth data or to compute numerical derivatives, preserving features of the distribution like relative maxima and minima better than moving average approaches.

Wraps scipy.signal.savgol_filter.

Parameters:

window_lengthint, default=5: Length of the filter window. Must be a positive odd integer.
polyorderint, default=2: Order of the polynomial used to fit the samples. Must be less than window_length.
derivint, default=0: Order of the derivative to compute. Use 0 to simply smooth the data without differentiation.
deltafloat, default=1.0: Spacing of the samples to which the filter will be applied. Only relevant when deriv > 0.
modestr, default=”interp”: How to extend the signal at the boundaries. One of "interp", "mirror", "nearest", "wrap", "constant".
cvalfloat, default=0.0: Value to fill past the edges of the input when mode is "constant".

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.savitzky_golay import (
...     SavitzkyGolayTransformer,
... )
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> t = SavitzkyGolayTransformer(window_length=7, polyorder=2)
>>> y_smooth = t.fit_transform(y)

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.