# Effect of strong auto-correlation on forecasting?

Suppose a wise-sense stationary univariate time series has relatively strong auto-correlation of lag-length of 1, say, around -0.7

Then how would it affect the forecast?

Conversely, if a stochastic process were to be IID, then due to a law of large number, $E[X] = {1 \over N} \sum X$ as N gets large and thus you could say that the "best guess" of the next data point given the previous data points up until now is simply the mean average of the previous points.

But if such a strong auto-correlation were to be present, then I believe it's possible to come up with a "best guess" that's even more accurate than simply using the mean average of the previous points.

I was thinking of an autoregressive model but what are some common methods?