If not, even if you measure the same thing, the variance may differ - you can start applying the law of large numbers since you mentionned "millions" and just consider a normal distribution, but for rare events you need something different (Poisson)
Are the series static? If not (ex: temperature) you will have to model on time. As the link mentions people crossing intersection, I assume more people will at 9am than at 2am (7 hours ago). Likewise if one series is a weekday and the other a weekend, you may have surprises!
You have to consider other things (ex: day of the week).
I would recommend avoiding the problem altogether: fit something (KDE, ARIMA...) on your timeseries, and plot the fit with the 95% CI from X=0 to X=24
Do the same for the other series, from X=0 to X=7 and see if they are at least in the same CI
Then compute the correlation of the 2 fits (just to have a reference number) and use that as a metric.