Hey there, collective of knowledge! I'm looking into using ML to analyze telemetry data to determine a state from data over time. It does not need to be a predictive model, just learn the "signs", so to speak, to be able to judge what state (and at what confidence it thinks it is correct).

The data is *nearly* good enough to have programming logic be able to determine the current state, but not 100% reliable.

I was thinking that the CSV data I have from telemetry (as well as new telemetry) can be marked up with what state it is in at the time of recording (rows are basically samples at 60Hz rate), and is pretty easy to mark up from a human perspective, since state changes normally takes place at 1-2 minute intervals (if that), with a few states lasting some 20-30 seconds. I surmise that this data could be used for the training phase, and I am specifically looking for finding the state **changes** when that happens.

I can easily create realistic sample data with markup, which I assume is step 1.

Target is to be of use in Apple's eco system, but I have very little idea of what kind of training of the ML model is best for such practice as this. I suspect that the model would need a sample size, time-wise, of say 60 seconds to compare with real-time live data.

Any help, pointers, advice, links, resources and such is appreciated!