ddtw_alignment_path

ddtw_alignment_path(x: ndarray, y: ndarray, window: float | None = None, itakura_max_slope: float | None = None) tuple[list[tuple[int, int]], float][source]

Compute the DDTW alignment path between two time series.

Parameters:
xnp.ndarray

First time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).

ynp.ndarray

Second time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).

windowfloat, default=None

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

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.

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 ddtw distance betweeen the two time series.

Raises:
ValueError

If x and y are not 1D, or 2D arrays. If n_timepoints or m_timepoints are less than 2.

Examples

>>> import numpy as np
>>> from aeon.distances import ddtw_alignment_path
>>> x = np.array([[1, 2, 3, 6]])
>>> y = np.array([[1, 2, 3, 4]])
>>> ddtw_alignment_path(x, y)
([(0, 0), (1, 1)], 0.25)