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I have N time series of DIFFERENT lengths with i number of data points in each observation.

I would like to compute the similarity of each time series and generate M number of clusters.

I have tried Jaquard simliarity, and followed the link below

Time Series Similarity : Differing Lengths with R

but am looking for a similar solution in Python here (which I have found https://github.com/slaypni/fastdtw)

The other problem is the NUMBER of features in the dataset (it is not just X & Y). X = Time, but I have 5 vectors for Y.

import numpy as np
from scipy.spatial.distance import euclidean

from fastdtw import fastdtw
x = x
y = y
print(x.shape)
print(y.shape)
distance, path = fastdtw(x, y, dist=euclidean)
print(distance)

Works fine for uneven dimension size, but if I add multiple dimensions it kicks out an error. Should I do some transposing?

Finally, if I can calculate the distance between each pair, what is the best way to cluster them?

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