tslearn.piecewise
.PiecewiseAggregateApproximation¶

class
tslearn.piecewise.
PiecewiseAggregateApproximation
(n_segments=1)[source]¶ Piecewise Aggregate Approximation (PAA) transformation.
PAA was originally presented in [1].
Parameters:  n_segments : int (default: 1)
Number of PAA segments to compute
Notes
This method requires a dataset of equalsized 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. ]], <BLANKLINE> [[ 2.1 ], [2. ], [ 0. ]]]) >>> paa.distance_paa(paa_data[0], paa_data[1]) # doctest: +ELLIPSIS 3.15039... >>> paa.distance(data[0], data[1]) # doctest: +ELLIPSIS 3.15039... >>> paa.inverse_transform(paa_data) array([[[ 0.5 ], [ 0.5 ], [0.45], [0.45], [ 0. ], [ 0. ]], <BLANKLINE> [[ 2.1 ], [ 2.1 ], [2. ], [2. ], [ 0. ], [ 0. ]]])
Methods
distance
(self, ts1, ts2)Compute distance between PAA representations as defined in [1]. distance_paa
(self, paa1, paa2)Compute distance between PAA representations as defined in [1]. fit
(self, X[, y])Fit a PAA representation. fit_transform
(self, 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_params
(self[, deep])Get parameters for this estimator. inverse_transform
(self, X)Compute time series corresponding to given PAA representations. set_params
(self, **params)Set the parameters of this estimator. to_hdf5
(self, path)Save model to a HDF5 file. to_json
(self, path)Save model to a JSON file. to_pickle
(self, path)Save model to a pickle file. transform
(self, X[, y])Transform a dataset of time series into its PAA representation. 
distance
(self, ts1, ts2)[source]¶ Compute distance between PAA representations as defined in [1].
Parameters:  ts1 : arraylike
A time series
 ts2 : arraylike
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
(self, paa1, paa2)[source]¶ Compute distance between PAA representations as defined in [1].
Parameters:  paa1 : arraylike
PAA representation of a time series
 paa2 : arraylike
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
(self, X, y=None)[source]¶ Fit a PAA representation.
Parameters:  X : arraylike of shape (n_ts, sz, d)
Time series dataset
Returns:  PiecewiseAggregateApproximation
self

fit_transform
(self, X, y=None, **fit_params)[source]¶ Fit a PAA representation and transform the data accordingly.
Parameters:  X : arraylike of shape (n_ts, sz, d)
Time series dataset
Returns:  numpy.ndarray of shape (n_ts, n_segments, d)
PAATransformed dataset

classmethod
from_hdf5
(path)[source]¶ Load model from a HDF5 file. Requires
h5py
http://docs.h5py.org/Parameters:  path : str
Full path to file.
Returns:  Model instance

classmethod
from_json
(path)[source]¶ Load model from a JSON file.
Parameters:  path : str
Full path to file.
Returns:  Model instance

classmethod
from_pickle
(path)[source]¶ Load model from a pickle file.
Parameters:  path : str
Full path to file.
Returns:  Model instance

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

inverse_transform
(self, X)[source]¶ Compute time series corresponding to given PAA representations.
Parameters:  X : arraylike 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_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters:  **params : dict
Estimator parameters.
Returns:  self : object
Estimator instance.

to_hdf5
(self, path)[source]¶ Save model to a HDF5 file. Requires
h5py
http://docs.h5py.org/Parameters:  path : str
Full file path. File must not already exist.
Raises:  FileExistsError
If a file with the same path already exists.