lcss_alignment_path(x: ndarray, y: ndarray, window: float | None = None, epsilon: float = 1.0, itakura_max_slope: float | None = None) Tuple[List[Tuple[int, int]], float][source]

Compute the LCSS alignment path between two time series.


First time series, shape (n_channels, n_timepoints) or (n_timepoints,).


Second time series, shape (m_channels, m_timepoints) or (m_timepoints,).

windowfloat, default=None

The window to use for the bounding matrix. If None, no bounding matrix is used.

epsilonfloat, default=1.

Matching threshold to determine if two subsequences are considered close enough to be considered ‘common’. The default is 1.

itakura_max_slopefloat, default=None

Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1.

List[Tuple[int, int]]

The alignment path between the two time series where each element is a tuple of the index in x and the index in y that have the best alignment according to the cost matrix.


The LCSS distance between the two time series.


If x and y are not 1D or 2D arrays.


>>> import numpy as np
>>> from aeon.distances import lcss_alignment_path
>>> x = np.array([[1, 2, 3, 6]])
>>> y = np.array([[1, 2, 3, 4]])
>>> path, dist = lcss_alignment_path(x, y)
>>> path
[(0, 0), (1, 1), (2, 2)]