That's what I get for picking an example from a domain I do not understand well! So perhaps I'll relax my statement. Time is one of a large number of explanatory variables. The amount of information you can extract from it will be limited.

Yah, sorry for taking such a rough shot at you.

Your point is incredibly valuable, and if I wasn't in a hurry I probably would have brought this up:

Most time series analysis, especially when using ML is ideal when the time part is stripped out through cleaver feature engineering. This isn't always possible, which is why the stock market is a pain and ML often doesn't work on it.

I do time series predictive analytics for a living and most of what I do is clever feature engineering trying to strip the time domain out of it and also reducing the number of points as much as possible. The less features, the smaller your training set needs to be and the higher your accuracy will be. Note: This is time series classification, not time series forecasting.

To extend your point, time lag is a feature in ARIMA and statistical time series model.

What you're doing is looking at autocorrelation base on time lags. That's what ACF and PACF graphs is displaying.