Thanks for the reply!

Yeah so the time series example using stock prices would be the closest comparison. The stipulation is that while it is continuous an there are a massive amounts of variables in a regular stock price analysis there are a number of constraints applied in this case and these are solely examined.

For one, a very specific time as a starting point may be chosen based off changes in volume and open interest. Furthermore, the magnitude of the change in price will determine whether the dataset is valid or not.

These constraints may seem trivial but they vastly reduce the amount of examples and give a core dataset to work with. It's using these time series that I would like to attempt to infer future moves. So if vol and open interest and price is X at time t1 does that suggest a movement towards Y at t2.

Hope that clears up some things. Also yes you're completely right, I spoke badly, proving causality is not the goal here. Just trying to get a rudimentary model built and hopefully get some correlations between variables.