I understood ACF usefulness, with a temperature example... If we have a temperature time series, it is useful to know (for example) how correlated the temperature of past month is with this month.

But I do not get the logic behind PACF... PACF in my example would be, using July's temperature to predict September, given we know August's temperature.

What does it mean that we can "control" the other lags?

In Wikipedia it says

PACF contrasts with the autocorrelation function, which does not control for other lags

Unfortunately I cannot relate this to a real life example.

  • $\begingroup$ PACF is just the partial autocorrelation function, related to partial correlation, see stats.stackexchange.com/questions/56969/… $\endgroup$ – user2974951 Sep 20 '19 at 13:00
  • $\begingroup$ I was looking for a real life example of PACF use. $\endgroup$ – Chicago1988 Sep 21 '19 at 14:37
  • $\begingroup$ There is an example on the Wikipedia page. $\endgroup$ – user2974951 Sep 23 '19 at 10:51

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