# Stationarizer¶

class gtda.time_series.Stationarizer(operation='return')[source]

Methods for stationarizing time series data.

Time series may be stationarized to remove or reduce linear or exponential trends.

Parameters

operation ('return' | 'log-return', default: 'return') –

The type of stationarization operation to perform. It can have two values:

• 'return': This option transforms the time series $${X_t}_t$$ into the time series of relative returns, i.e. the ratio $$(X_t-X_{ t-1})/X_t$$.

• 'log-return': This option transforms the time series $${X_t}_t$$ into the time series of relative log-returns, i.e. $$\log(X_t/X_{ t-1})$$.

Examples

>>> import numpy as np
>>> from gtda.time_series import Stationarizer
>>> # Create a noisy signal
>>> signal = np.asarray([np.sin(x /40) + 5 + np.random.random()
>>>                      for x in range(0, 300)]).reshape(-1, 1)
>>> # Initialize the stationarizer
>>> stationarizer = Stationarizer(operation='return')
>>> # Fit and transform the signal
>>> signal_stationarized = stationarizer.fit_transform(signal)
>>> print(signal_stationarized.shape)
(299,)

__init__(operation='return')[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]

Do nothing and return the estimator unchanged.

This method is here to implement the usual scikit-learn API and hence work in pipelines.

Parameters
• X (ndarray of shape (n_samples,) or (n_samples, ..)) – Input data.

• y (None) – Ignored.

Returns

self

Return type

object

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
• X (ndarray of shape (n_samples, ..)) – Input data.

• y (None) – There is no need for a target, yet the pipeline API requires this parameter.

Returns

Xt – Transformed input.

Return type

numpy array of shape (n_samples, ..)

fit_transform_resample(X, y, **fit_params)

Fit to data, then transform the input and resample the target. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X ans a resampled version of y.

Parameters
• X (ndarray of shape (n_samples, ..)) – Input data.

• y (ndarray of shape (n_samples,)) – Target data.

Returns

• Xt (ndarray of shape (n_samples, …)) – Transformed input.

• yr (ndarray of shape (n_samples, …)) – Resampled target.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

resample(y, X=None)[source]

Resample y.

Parameters
• y (ndarray of shape (n_samples,)) – Target.

• X (None) – There is no need for input data, yet the pipeline API requires this parameter.

Returns

yr – Resampled target. n_samples_new = n_samples - 1.

Return type

ndarray of shape (n_samples_new,)

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

object

transform(X, y=None)[source]

Stationarize X by applying the procedure given by operation.

Parameters
• X (ndarray of shape (n_samples,) or (n_samples, ..)) – Input data.

• y (None) – There is no need for a target, yet the pipeline API requires this parameter.

Returns

Xt – Stationarized array. n_samples_new = n_samples - 1.

Return type

ndarray of shape (n_samples_new, ..)

transform_resample(X, y)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
• X (ndarray of shape (n_samples, ..)) – Input data.

• y (ndarray of shape (n_samples,)) – Target data.

Returns

• Xt (ndarray of shape (n_samples, …)) – Transformed input.

• yr (ndarray of shape (n_samples, …)) – Resampled target.