lcss_alignment_path

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

Compute the lcss alignment path between two time series.

Parameters:

x: np.ndarray, of shape (n_channels, n_timepoints) or (n_timepoints,)

First time series.

y: np.ndarray, of shape (m_channels, m_timepoints) or (m_timepoints,)

Second time series.

window: float, default=None

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

epsilon: float, defaults=1.

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

Returns:

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.

float

The lcss distance between the two time series.

Raises:

ValueError

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

Examples

>>> 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)]