TimeSeriesScalerMeanVariance#

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

Scaler for time series datasets. Scales fetures values so that their mean (resp. standard deviation) in given dimensions is mu (resp. std).

Parameters:

mufloat (default: 0.): Mean of the output time series.
stdfloat (default: 1.): Standard deviation of the output time series.
per_timeseries: bool (default: True): Whether the scaling should be performed per time series.
per_feature: bool (default: True): Whether the scaling should be performed per feature. Meaningless for univariate timeseries.

Notes

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.]]])
>>> TimeSeriesScalerMeanVariance(per_timeseries=False,
...                              per_feature=False
... ).fit_transform([[[1, 2], [2, 3]], [[3, 4], [4, 5]]])
array([[[-1.63299316, -0.81649658],
        [-0.81649658,  0.        ]],

       [[ 0.        ,  0.81649658],
        [ 0.81649658,  1.63299316]]])

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_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`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:

Xarray-like of shape (n_ts, sz, d): Time series dataset to be rescaled.

Returns:

numpy.ndarray: Resampled time series dataset.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)#

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.

Parameters: