alignment_path

alignment_path(x: ndarray, y: ndarray, metric: str, **kwargs: Any) → Tuple[List[Tuple[int, int]], float]

Compute the alignment path and distance 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.

metric: str

The distance metric to use. The value must be one of the following strings: ‘euclidean’, ‘squared’, ‘dtw’, ‘ddtw’, ‘wdtw’, ‘wddtw’, ‘lcss’, ‘edr’, ‘erp’, ‘msm’

kwargs: Any

Arguments for metric. Refer to each metrics documentation for a list of possible arguments.

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 dtw distance betweeen the two time series.

Raises:

ValueError

If x and y are not 1D, or 2D arrays. If metric is not one of the supported strings or a callable.

Examples

>>> import numpy as np
>>> from aeon.distances import alignment_path
>>> x = np.array([[1, 2, 3, 6]])
>>> y = np.array([[1, 2, 3, 4]])
>>> alignment_path(x, y, metric='dtw')
([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0)