tslearn.piecewise.PiecewiseAggregateApproximation

class tslearn.piecewise.PiecewiseAggregateApproximation(n_segments=1)[source]

Piecewise Aggregate Approximation (PAA) transformation.

PAA was originally presented in [1].

Parameters:
n_segmentsint (default: 1)

Number of PAA segments to compute

Notes

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

References

[1]

E. Keogh & M. Pazzani. Scaling up dynamic time warping for datamining applications. SIGKDD 2000, pp. 285–289.

Examples

>>> paa = PiecewiseAggregateApproximation(n_segments=3)
>>> data = [[-1., 2., 0.1, -1., 1., -1.], [1., 3.2, -1., -3., 1., -1.]]
>>> paa_data = paa.fit_transform(data)
>>> paa_data.shape
(2, 3, 1)
>>> paa_data
array([[[ 0.5 ],
        [-0.45],
        [ 0.  ]],

       [[ 2.1 ],
        [-2.  ],
        [ 0.  ]]])
>>> paa.distance_paa(paa_data[0], paa_data[1])  
3.15039...
>>> paa.distance(data[0], data[1])  
3.15039...
>>> paa.inverse_transform(paa_data)
array([[[ 0.5 ],
        [ 0.5 ],
        [-0.45],
        [-0.45],
        [ 0.  ],
        [ 0.  ]],

       [[ 2.1 ],
        [ 2.1 ],
        [-2.  ],
        [-2.  ],
        [ 0.  ],
        [ 0.  ]]])

Methods

distance(ts1, ts2)

Compute distance between PAA representations as defined in [1].

distance_paa(paa1, paa2)

Compute distance between PAA representations as defined in [1].

fit(X[, y])

Fit a PAA representation.

fit_transform(X[, y])

Fit a PAA representation and transform the data accordingly.

from_hdf5(path)

Load model from a HDF5 file.

from_json(path)

Load model from a JSON file.

from_pickle(path)

Load model from a pickle file.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X)

Compute time series corresponding to given PAA representations.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

to_hdf5(path)

Save model to a HDF5 file.

to_json(path)

Save model to a JSON file.

to_pickle(path)

Save model to a pickle file.

transform(X[, y])

Transform a dataset of time series into its PAA representation.
distance(ts1, ts2)[source]

Compute distance between PAA representations as defined in [1].

Parameters:
ts1array-like

A time series

ts2array-like

Another time series

Returns:
float

PAA distance

References

[1] (1,2)

E. Keogh & M. Pazzani. Scaling up dynamic time warping for datamining applications. SIGKDD 2000, pp. 285–289.

distance_paa(paa1, paa2)[source]

Compute distance between PAA representations as defined in [1].

Parameters:
paa1array-like

PAA representation of a time series

paa2array-like

PAA representation of another time series

Returns:
float

PAA distance

References

[1] (1,2)

E. Keogh & M. Pazzani. Scaling up dynamic time warping for datamining applications. SIGKDD 2000, pp. 285–289.

fit(X, y=None)[source]

Fit a PAA representation.

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

Time series dataset

Returns:
PiecewiseAggregateApproximation

self

fit_transform(X, y=None, **fit_params)[source]

Fit a PAA representation and transform the data accordingly.

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

Time series dataset

Returns:
numpy.ndarray of shape (n_ts, n_segments, d)

PAA-Transformed dataset

classmethod from_hdf5(path)[source]

Load model from a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters:
pathstr

Full path to file.

Returns:
Model instance
classmethod from_json(path)[source]

Load model from a JSON file.

Parameters:
pathstr

Full path to file.

Returns:
Model instance
classmethod from_pickle(path)[source]

Load model from a pickle file.

Parameters:
pathstr

Full path to file.

Returns:
Model instance
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.

inverse_transform(X)[source]

Compute time series corresponding to given PAA representations.

Parameters:
Xarray-like of shape (n_ts, sz_paa, d)

A dataset of PAA series.

Returns:
numpy.ndarray of shape (n_ts, sz_original_ts, d)

A dataset of time series corresponding to the provided representation.

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.

to_hdf5(path)[source]

Save model to a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters:
pathstr

Full file path. File must not already exist.

Raises:
FileExistsError

If a file with the same path already exists.

to_json(path)[source]

Save model to a JSON file.

Parameters:
pathstr

Full file path.

to_pickle(path)[source]

Save model to a pickle file.

Parameters:
pathstr

Full file path.

transform(X, y=None)[source]

Transform a dataset of time series into its PAA representation.

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

Time series dataset

Returns:
numpy.ndarray of shape (n_ts, n_segments, d)

PAA-Transformed dataset

Examples using tslearn.piecewise.PiecewiseAggregateApproximation

PAA and SAX features

PAA and SAX features