giotto.time_series.Stationarizer¶
-
class
giotto.time_series.Stationarizer(operation='return')¶ 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. \(\log(X_t/X_{ t-1})\).
- operation
Examples
>>> import numpy as np >>> from giotto.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,)
Methods
fit(self, X[, y])Do nothing and return the estimator unchanged.
fit_transform(self, X[, y])Fit to data, then transform it.
fit_transform_resample(self, X, y, …)Fit to data, then transform the input and resample the target.
get_params(self[, deep])Get parameters for this estimator.
resample(self, y[, X])Resample y.
set_params(self, \*\*params)Set the parameters of this estimator.
transform(self, X[, y])Stationarize X by applying the procedure given by operation.
transform_resample(self, X, y)Fit to data, then transform it.
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__init__(self, operation='return')¶ Initialize self. See help(type(self)) for accurate signature.
-
fit(self, X, y=None)¶ Do nothing and return the estimator unchanged.
This method is there to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
- Xndarray, shape (n_samples,) or (n_samples, …)
Input data.
- yNone
Ignored.
- Returns
- selfobject
-
fit_transform(self, 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
- Xndarray of shape (n_samples, …)
Input data.
- yNone
There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- Xtnumpy array of shape (n_samples, …)
Transformed input.
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fit_transform_resample(self, 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
- Xndarray of shape (n_samples, …)
Input data.
- yndarray of shape (n_samples, )
Target data.
- Returns
- Xtndarray of shape (n_samples, …)
Transformed input.
- yrndarray of shape (n_samples, …)
Resampled target.
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get_params(self, deep=True)¶ Get parameters for this estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
resample(self, y, X=None)¶ Resample y.
- Parameters
- yndarray, shape (n_samples,)
Target.
- XNone
There is no need for input data, yet the pipeline API requires this parameter.
- Returns
- yrndarray, shape (n_samples_new,)
Resampled target.
n_samples_new = n_samples - 1.
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set_params(self, **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.- Returns
- self
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transform(self, X, y=None)¶ Stationarize X by applying the procedure given by operation.
- Parameters
- Xndarray, shape (n_samples,) or (n_samples, …)
Input data.
- yNone
There is no need for a target, yet the pipeline API requires this parameter.
- Returns
- Xtndarray, shape (n_samples_new, …)
Stationarized array.
n_samples_new = n_samples - 1.
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transform_resample(self, 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
- Xndarray of shape (n_samples, …)
Input data.
- yndarray of shape (n_samples, )
Target data.
- Returns
- Xtndarray of shape (n_samples, …)
Transformed input.