`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.

Examples

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

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.
`transform`(self, X, \\kwargs)	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, **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 : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.