Efficient pairwise DTW calculation using numpy or cython

Question

I am trying to calculate the pairwise distances between multiple time-series contained in a numpy array. Please see the code below

print(type(sales))
print(sales         


        

Answer 1

To be honest, fastdtw is not fast at all

from cdtw import pydtw
from dtaidistance import dtw
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean
s1=np.array([1,2,3,4],dtype=np.double)
s2=np.array([4,3,2,1],dtype=np.double)

%timeit dtw.distance_fast(s1, s2)
4.1 µs ± 28.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit d2 = pydtw.dtw(s1,s2,pydtw.Settings(step = 'p0sym', window = 'palival', param = 2.0, norm = False, compute_path = True)).get_dist()
45.6 µs ± 3.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit d3,_=fastdtw(s1, s2, dist=euclidean)
901 µs ± 9.95 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

fastdtw is 219 times slower than dtaidistance lib and 20x slower than cdtw

Consider changing. Here is dtaidistance git:

https://github.com/wannesm/dtaidistance

To install, just:

pip install dtaidistance