PiecewiseAggregateApproximation

class tslearn.piecewise.PiecewiseAggregateApproximation(n_segments=1)

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.  ]]])
>>> float(paa.distance_paa(paa_data[0], paa_data[1]))
3.15039...
>>> float(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)

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)

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)

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)

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)

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)

Load model from a JSON file.

Parameters:

pathstr

Full path to file.

Returns:

Model instance

classmethod from_pickle(path)

Load model from a pickle file.

Parameters:

pathstr

Full path to file.

Returns:

Model instance

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.

inverse_transform(X)

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)

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

to_hdf5(path)

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)

Save model to a JSON file.

Parameters:

pathstr

Full file path.

to_pickle(path)

Save model to a pickle file.

Parameters:

pathstr

Full file path.

transform(X, y=None)

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