I have a plurality of timeseries of angular data. These values are not vectors (no magnitude), just angles. I need to determine among the various timeseries how correlated they are with each other (e.g., would like to obtain a correlation matrix) over the duration of the data. For example, some are measured very close to each other and I expect will be highly correlated, but I'm interested in also seeing how correlated the further measurements are.
How would I go about adapting this angular data in order to be able to obtain a correlation matrix? I thought about just vectorizing it (i.e., with unit vectors), but then I'm not sure how to do the correlation analysis with this two-dimensional data, as I've only done it with one dimensional previously. Of course, I can't simply analyze the correlation of the angles themselves, due to the nature of angular data (the reset at 0-360).
I'm working in Python, so if anyone has any recommendations on relevant packages I would appreciate it.
I have found a solution in the Astropy python package. The following function is suitable for circular correlation: https://docs.astropy.org/en/stable/api/astropy.stats.circcorrcoef.html