Distances

The aeon.distances module contains time series specific distance functions that can be used in aeon and scikit learn estimators. It also contains tools for extracting the alignment paths for a distance calculation between two series, and tools for finding all pairwise distances

Amerced Dynamic Time Warping (ADTW)

adtw_distance(x, y[, window, ...])	Compute the ADTW distance between two time series.
adtw_pairwise_distance(X[, y, window, ...])	Compute the ADTW pairwise distance between a set of time series.
adtw_cost_matrix(x, y[, window, ...])	Compute the ADTW cost matrix between two time series.
adtw_alignment_path(x, y[, window, ...])	Compute the ADTW alignment path between two time series.

Derivative Dynamic Time Warping (DDTW)

ddtw_distance(x, y[, window, itakura_max_slope])	Compute the DDTW distance between two time series.
ddtw_pairwise_distance(X[, y, window, ...])	Compute the DDTW pairwise distance between a set of time series.
ddtw_cost_matrix(x[, y, window, ...])	Compute the DDTW cost matrix between two time series.
ddtw_alignment_path(x, y[, window, ...])	Compute the DDTW alignment path between two time series.

Dynamic Time Warping (DTW)

dtw_distance(x, y[, window, itakura_max_slope])	Compute the DTW distance between two time series.
dtw_pairwise_distance(X[, y, window, ...])	Compute the DTW pairwise distance between a set of time series.
dtw_cost_matrix(x, y[, window, ...])	Compute the DTW cost matrix between two time series.
dtw_alignment_path(x, y[, window, ...])	Compute the DTW alignment path between two time series.

Edit Real Penalty (ERP)

erp_distance(x, y[, window, g, g_arr, ...])	Compute the ERP distance between two time series.
erp_pairwise_distance(X[, y, window, g, ...])	Compute the ERP pairwise distance between a set of time series.
erp_cost_matrix(x, y[, window, g, g_arr, ...])	Compute the ERP cost matrix between two time series.
erp_alignment_path(x, y[, window, g, g_arr, ...])	Compute the ERP alignment path between two time series.

Edit distance for real sequences (EDR)

edr_distance(x, y[, window, epsilon, ...])	Compute the EDR distance between two time series.
edr_pairwise_distance(X[, y, window, ...])	Compute the pairwise EDR distance between a set of time series.
edr_cost_matrix(x, y[, window, epsilon, ...])	Compute the EDR cost matrix between two time series.
edr_alignment_path(x, y[, window, epsilon, ...])	Compute the EDR alignment path between two time series.

Euclidean

euclidean_distance(x, y)	Compute the Euclidean distance between two time series.
euclidean_pairwise_distance(X[, y])	Compute the Euclidean pairwise distance between a set of time series.

Longest Common Subsequence (LCSS)

lcss_distance(x, y[, window, epsilon, ...])	Return the LCSS distance between x and y.
lcss_pairwise_distance(X[, y, window, ...])	Compute the LCSS pairwise distance between a set of time series.
lcss_cost_matrix(x, y[, window, epsilon, ...])	Return the LCSS cost matrix between x and y.
lcss_alignment_path(x, y[, window, epsilon, ...])	Compute the LCSS alignment path between two time series.

Manhattan

manhattan_distance(x, y)	Compute the manhattan distance between two time series.
manhattan_pairwise_distance(X[, y])	Compute the manhattan pairwise distance between a set of time series.

Minkowski

minkowski_distance(x, y[, p, w])	Compute the Minkowski distance between two time series.
minkowski_pairwise_distance(X[, y, p, w])	Compute the Minkowski pairwise distance between a set of time series.

Move-Split-Merge (MSM)

msm_distance(x, y[, window, independent, c, ...])	Compute the MSM distance between two time series.
msm_pairwise_distance(X[, y, window, ...])	Compute the msm pairwise distance between a set of time series.
msm_cost_matrix(x, y[, window, independent, ...])	Compute the MSM cost matrix between two time series.
msm_alignment_path(x, y[, window, ...])	Compute the msm alignment path between two time series.

Shape-based Distance (SBD)

sbd_distance(x, y[, standardize])	Compute the shape-based distance (SBD) between two time series.
sbd_pairwise_distance(X[, y, standardize])	Compute the shape-based distance (SBD) between all pairs of time series.

Shape Dynamic Time Warping (Shape DTW)

shape_dtw_distance(x, y[, window, ...])	Compute the ShapeDTW distance function between two series x and y.
shape_dtw_pairwise_distance(X[, y, window, ...])	Compute the ShapeDTW pairwise distance among a set of series.
shape_dtw_cost_matrix(x, y[, window, ...])	Compute the ShapeDTW cost matrix between two series x and y.
shape_dtw_alignment_path(x, y[, window, ...])	Compute the ShapeDTW alignment path between two series x and y.

Squared

squared_distance(x, y)	Compute the squared distance between two time series.
squared_pairwise_distance(X[, y])	Compute the squared pairwise distance between a set of time series.

Time Warp Edit (TWE)

twe_distance(x, y[, window, nu, lmbda, ...])	Compute the TWE distance between two time series.
twe_pairwise_distance(X[, y, window, nu, ...])	Compute the TWE pairwise distance between a set of time series.
twe_cost_matrix(x, y[, window, nu, lmbda, ...])	Compute the TWE cost matrix between two time series.
twe_alignment_path(x, y[, window, nu, ...])	Compute the TWE alignment path between two time series.

Weighted Derivative Dynamic Time Warping (WDDTW)

wddtw_distance(x, y[, window, g, ...])	Compute the WDDTW distance between two time series.
wddtw_pairwise_distance(X[, y, window, g, ...])	Compute the WDDTW pairwise distance between a set of time series.
wddtw_cost_matrix(x, y[, window, g, ...])	Compute the WDDTW cost matrix between two time series.
wddtw_alignment_path(x, y[, window, g, ...])	Compute the WDDTW alignment path between two time series.

Weighted Dynamic Time Warping (DTW)

wdtw_distance(x, y[, window, g, ...])	Compute the WDTW distance between two time series.
wdtw_pairwise_distance(X[, y, window, g, ...])	Compute the WDTW pairwise distance between a set of time series.
wdtw_cost_matrix(x, y[, window, g, ...])	Compute the WDTW cost matrix between two time series.
wdtw_alignment_path(x, y[, window, g, ...])	Compute the WDTW alignment path between two time series.

General methods with distance argument

distance(x, y, metric, **kwargs)	Compute the distance between two time series.
pairwise_distance(x[, y, metric])	Compute the pairwise distance matrix between two time series.
cost_matrix(x, y, metric, **kwargs)	Compute the alignment path and distance between two time series.
alignment_path(x, y, metric, **kwargs)	Compute the alignment path and distance between two time series.
create_bounding_matrix(x_size, y_size[, ...])	Create a bounding matrix for an elastic distance.

General methods to recover distance functions

get_distance_function(metric)	Get the distance function for a given metric string or callable.
get_pairwise_distance_function(metric)	Get the pairwise distance function for a given metric string or callable.
get_distance_function_names()	Get a list of distance function names in aeon.
get_cost_matrix_function(metric)	Get the cost matrix function for a given metric string or callable.
get_alignment_path_function(metric)	Get the alignment path function for a given metric string or callable.