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?
To calculate the (estimated) value of the monthly/weekly unique visitors based on the per day count you may use the HyperLogLog algorihm.

This will give you the historical values and you can use any time series approach to predict future counts