tslearn.preprocessing.TimeSeriesScalerMeanVariance

class tslearn.preprocessing.TimeSeriesScalerMeanVariance(mu=0.0, std=1.0)[source]

Scaler for time series. Scales time series so that their mean (resp. standard deviation) in each dimension is mu (resp. std).

Parameters:
mu : float (default: 0.)

Mean of the output time series.

std : float (default: 1.)

Standard deviation of the output time series.

Notes

This method requires a dataset of equal-sized time series.

NaNs within a time series are ignored when calculating mu and std.

Examples

>>> TimeSeriesScalerMeanVariance(mu=0.,
...                              std=1.).fit_transform([[0, 3, 6]])
array([[[-1.22474487],
        [ 0.        ],
        [ 1.22474487]]])
>>> TimeSeriesScalerMeanVariance(mu=0.,
...                              std=1.).fit_transform([[numpy.nan, 3, 6]])
array([[[nan],
        [-1.],
        [ 1.]]])

Methods

fit(self, X[, y]) A dummy method such that it complies to the sklearn requirements.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, **params) Set the parameters of this estimator.
transform(self, X[, y]) Fit to data, then transform it.
fit(self, X, y=None, **kwargs)[source]

A dummy method such that it complies to the sklearn requirements. Since this method is completely stateless, it just returns itself.

Parameters:
X

Ignored

Returns:
self
fit_transform(self, X, y=None, **kwargs)[source]

Fit to data, then transform it.

Parameters:
X : array-like of shape (n_ts, sz, d)

Time series dataset to be rescaled.

Returns:
numpy.ndarray

Resampled time series dataset.

get_params(self, deep=True)[source]

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 : mapping of string to any

Parameter names mapped to their values.

set_params(self, **params)[source]

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 : object

Estimator instance.

transform(self, X, y=None, **kwargs)[source]

Fit to data, then transform it.

Parameters:
X : array-like of shape (n_ts, sz, d)

Time series dataset to be rescaled

Returns:
numpy.ndarray

Rescaled time series dataset