I’ve recently encountered some of the point cloud to persistence diagram representations in time series analysis, and they seem very interesting, if not familiar to those of us who ended up here from a computational/algorithmic background.

I’ve so far primarily encountered these topics written on in, naturally, topology and analysis settings, but was wondering if there’s yet been heavy adaptation in the ML sphere? With an influx of interest in geometric/topological domains these days, I would think this would be a popular topic.

So I suppose that my objective is to learn from more informed people on some of the relevant existing work in ML, as well as what we know of the strengths and criticisms regarding these representations of time series.