ts-dist

The module contains Python implementations of distance measures between two real time series feature vectors. Faster Cython implementations are also provided.

Distance measures

The following distance measures have been implemented.

• Dynamic Time Warping (DTW) [1]
• Longest Common Subsequence (LCSS) [2]
• Edit Distance on Real sequence (EDR) [3]

Example

import numpy as np
import pyximport; pyximport.install()

from ts_dist import dtw_dist as dtw_dist_py
from ts_dist import lcss_dist as lcss_dist_py
from ts_dist import edr_dist as edr_dist_py

from ts_dist_cy import dtw_dist as dtw_dist_cy
from ts_dist_cy import lcss_dist as lcss_dist_cy
from ts_dist_cy import edr_dist as edr_dist_cy

x = np.random.normal(0, 1, (1000))
y = np.random.normal(0, 1, (1000))

dtw_py = dtw_dist_py(x, y)
dtw_cy = dtw_dist_cy(x, y)

lcss_py = lcss_dist_py(x, y, delta=np.inf, epsilon=0.5)
lcss_cy = lcss_dist_cy(x, y, delta=np.inf, epsilon=0.5)

edr_py = edr_dist_py(x, y, epsilon=0.5)
edr_cy = edr_dist_cy(x, y, epsilon=0.5)

Benchmark

$ python benchmark_ts_dist.py
Average time taken for dtw_dist_py: 0.15160710269992705
Average time taken for dtw_dist_cy: 0.022138842300046237
Average time taken for lcss_dist_py: 0.10573908710066462
Average time taken for lcss_dist_cy: 0.031031618600536603
Average time taken for edr_dist_py: 0.13265878920065005
Average time taken for edr_dist_cy: 0.03591040430037538

Dependencies

• Python 3
• Cython
• Numpy
• Numba

References

[1] https://www.cs.unm.edu/~mueen/DTW.pdf

[2] M. Vlachos, G. Kollios and D. Gunopulos, "Discovering Similar Multidimensional Trajectories", 2002.

[3] L Chen, MT Özsu, V Oria, "Robust and fast similarity search for moving object trajectories", 2005.