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sktime

TSFeaturesTransformer

TSFeaturesTransformer

class TSFeaturesTransformer(features=None, scale=True)[source]

Transformer for extracting time series features via tsfeatures.

Direct interface to tsfeatures.tsfeatures [1] as an sktime transformer. This transformer works with Series with 1 column and a datetime index.

By default, this transformer uses 17 feature functions that extract approximately 34 features (42 for seasonal data with freq > 1). The default feature functions include:

Autocorrelation Features:

  • acf_features: Autocorrelation function features (6-7 features)

  • pacf_features: Partial autocorrelation function features (3-4 features)

Model-based Features:

  • arch_stat: ARCH model test statistic

  • heterogeneity: ARCH/GARCH heterogeneity features (4 features)

  • holt_parameters: Holt exponential smoothing parameters (2 features)

  • hw_parameters: Holt-Winters parameters (3 features, only if freq > 1)

  • stl_features: Seasonal-trend decomposition features (8-11 features)

Statistical Features:

  • crossing_points: Number of median crossings

  • entropy: Spectral entropy

  • flat_spots: Number of flat spots

  • hurst: Hurst exponent

  • lumpiness: Variance of variances across windows

  • nonlinearity: Terasvirta nonlinearity test

  • stability: Variance of means across windows

  • unitroot_kpss: KPSS unit root test statistic

  • unitroot_pp: Phillips-Perron unit root test statistic

Basic Features:

  • series_length: Length of the time series

Other supported features (non-default):

  • count_entropy: Entropy using only positive data.

  • intervals: Mean and Standard Deviation of intervals with positive values.

  • frequency: Wrapper of freq parameter.

  • guerrero: Applies Guerrero’s (1993) method to select the lambda which

minimises the coefficient of variation for subseries of x. - sparsity: Average obs with zero values.

Parameters:
featureslist of callable, optional

List of feature functions to compute. If None, uses default feature set.

scalebool, optional (default=True)

Whether to (mean-std) scale data before computing features.

Attributes:
is_fitted

Whether fit has been called.

References

Examples

>>> from sktime.transformations.tsfeatures import TSFeaturesTransformer
>>> from sktime.utils._testing.series import _make_series
>>> X = _make_series()
>>> transformer = TSFeaturesTransformer()
>>> Xt = transformer.fit_transform(X)
>>> # Example using specific features
>>> from tsfeatures.tsfeatures import acf_features
>>> acf_transformer = TSFeaturesTransformer(
...    features=[acf_features],
...    )
>>> acf_Xt = acf_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.