I have univariate time series data for 70 subjects sampled at 1000 Hz. When graphing the single subject plots, time is on the x-axis and amplitude (arbitarty unit) on the y-axis. When looking at the data, I'm seeing 3 "types" of subjects but there is no hard way of classifying the subjects. I want to use an unsupervised machine learning classification approach to put all of these subjects in 3 "classes" or "types" of subjects. I was thinking about using dynamic time warping or recurrent neural networks, but am not sure if this is the best approach. Could someone please help me with this? Maybe provide me with some info on how to construct these models/perform these analyses?
Thanks in advnace!