wildboar.annotate._segment#

Module Contents#

Functions#

segment([x, mpi, n_segments, window, exclude, ...])

Find change regimes in a time series.
wildboar.annotate._segment.segment(x=None, *, mpi=None, n_segments=1, window=0.1, exclude=0.2, boundry=1.0, return_arc_curve=False)[source]#

Find change regimes in a time series.

Parameters:

  • x (array-like of shape (n_samples, n_timestep) or (n_timestep, ), optional) – The time series. If x is given, the matrix profile of x is computed.

  • mpi (array-like of shape (n_samples, profile_size) or (profile_size), optional) – The matrix profile index. Must be given unless x is given.

  • n_segments (int, optional) – The number of segmentations to identify

  • window (int or float, optional) –

    The window size. The window parameter is ignored if mpi is not None.

    • if float, a fraction of n_timestep

    • if int, the exact window size

  • exclude (float, optional) – The self-join exclusion for the matrix profile. Ignored if mpi is given.

  • boundry (float, optional) – The region around an identified segmentation that is ignored when searching for subsequent segmentations

  • return_arc_curve (bool, optional) – Return the arc curve.

Returns:

  • segments (ndarray of shape (n_samples, n_segments), (n_segments) or int) – The start index of a segment

  • arc_curves (ndarray of shape (n_samples, profile_size) or (profile_size, )) – The arc curves

See also

wildboar.distance.matrix_profile

compute the matrix profile

References

Gharghabi, Shaghayegh, et al. (2017)

Matrix profile VIII: domain agnostic online semantic segmentation at superhuman performance levels. In proceedings of International Conference on Data Mining