`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`(X[, y])	A dummy method such that it complies to the sklearn requirements.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y])	Fit to data, then transform it.

fit(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(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(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 : dict Parameter names mapped to their values.

set_params(**params)[source]¶

Set the parameters of this estimator.

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