lcss_distance#

lcss_distance(x: ndarray, y: ndarray, window: float = None, epsilon: float = 1.0) → float[source]#

Return the lcss distance between x and y.

LCSS attempts to find the longest common sequence between two time series and returns a value that is the percentage that longest common sequence assumes. Originally present in [1], LCSS is computed by matching indexes that are similar up until a defined threshold (epsilon).

The value returned will be between 0.0 and 1.0, where 0.0 means the two time series are exactly the same and 1.0 means they are complete opposites.

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.
windowfloat, defaults=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:

float: The lcss distance between x and y.

Raises:

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

References

[1]

M. Vlachos, D. Gunopoulos, and G. Kollios. 2002. “Discovering Similar Multidimensional Trajectories”, In Proceedings of the 18th International Conference on Data Engineering (ICDE ‘02). IEEE Computer Society, USA, 673.

Examples

>>> import numpy as np
>>> from aeon.distances import lcss_distance
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[11, 12, 13, 14, 15, 16, 17, 18, 19, 20]])
>>> dist = lcss_distance(x, y)