# How to predict an approximate weekly/monthly number, when the Unique Daily Visitors for that week/month are already known

I am trying to come up with a formula or machine learning algorithm using which I can approximately predict the weekly or monthly users.

What to keep in mind is that I already have counts for the unique visitors per day for the week/months that I would like to make a near accurate prediction. Here, simply summing the daily unique users would not work, as they can be unique on one day but not on two days as they can have a session lasting over 2 days.

This method is to serve as an alternative to running a Spark job on the whole week/month data in order to save time and resources - Is this possible?

I have looked at Time Series and Linear Regression, but need more clarification on the possible approaches and also on any work-arounds?

You can't retrospectively do it just with counts of unique visitors per day. If you represent the unique users on each day by sets $A_1, A_2, \dots, A_n$, the union can be as small as $|A_1|$, if all sets are equal, or as large as $|A_1| + \dots + |A_n|$ if all sets a pairwise disjoint.
If you could estimate average number of days r per month a user visited the site (over the users that visited at least once), then there were exactly $(|A_1| + \dots + |A_n|)/r$ unique visitors that month.