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:
mufloat (default: 0.)

Mean of the output time series.

stdfloat (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_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()[source]

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)[source]

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)[source]

Set output container.

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

Parameters:
transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • None: Transform configuration is unchanged

Returns:
selfestimator instance

Estimator instance.

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.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

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

Rescaled time series dataset

Examples using tslearn.preprocessing.TimeSeriesScalerMeanVariance

Longest Common Subsequence

Longest Common Subsequence

LB_Keogh

LB_Keogh

sDTW multi path matching

sDTW multi path matching

Longest Commom Subsequence with a custom distance metric

Longest Commom Subsequence with a custom distance metric

DTW computation with a custom distance metric

DTW computation with a custom distance metric

1-NN with SAX + MINDIST

1-NN with SAX + MINDIST

KShape

KShape

Kernel k-means

Kernel k-means

k-means

k-means

Early Classification

Early Classification

Model Persistence

Model Persistence

PAA and SAX features

PAA and SAX features

Distance and Matrix Profiles

Distance and Matrix Profiles