Comment by quasse
3 days ago
> So we had to derive from them derived streams that didn't show temporal dependencies any longer, at least not as strongly
Could you expand on that (or at least point me towards some keywords to read up on)?
I think I understand conceptually what this means (network latency increases at noon CDT each day because YouTube load increases during the lunch hour, as an example) but I'm wondering how you normalize data streams for temporal dependencies with unknown frequencies (nominal change #1 happens each Sunday, while nominal change #2 happens each day at noon).
That's where you need time series models. Take a look at linear filters and how to handle seasonal variations.
It gets interesting when there are different frequencies at play simultaneously, like in your example -- day of the week and time of day effects. If their periods are mutually prime that's more convenient to handle as they are orthogonal in the Fourier space.
Think of these as radio AM modulation.