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I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be.

Depending on the visitor count on a day of the month, I would want to update the forecast for the monthly count.

Which method can I use to forecast the monthly number of visitors on a website using the same information on a daily level?

EDIT

I have data for six months at a day level. So for each day of the month, I have the daily unique visitor count and the total unique visitor count for each month.

This data is based on login data. I see how many unique logins are there for the day and that becomes the Daily uniq visitors.

I do the same process across a month to get Monthly uniq visitors

This is what the data looks like. enter image description here

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  • $\begingroup$ Umm...why not sum the numbers of visitors (predicted or actual, depending on what you have) over the days of the month? $\endgroup$
    – whuber
    Commented Jan 7, 2021 at 13:25
  • $\begingroup$ I could add that up but that would lead to double counting of users visiting the webpage. I am looking for monthly unique users. $\endgroup$ Commented Jan 7, 2021 at 13:57
  • $\begingroup$ You haven't described any kind of data that would enable you to do that. $\endgroup$
    – whuber
    Commented Jan 7, 2021 at 14:07
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    $\begingroup$ Monthly unique vistors: For a particular month, say July, I fetch data of the users that logged in at least once. From that pool, I see the distinct number of users for the month. That becomes monthly unique users. Yes, so for July there will be one value only because that is the truth value. I want to use the number of distinct transacting users for a given day to forecast what the monthly total will be. Thanks! $\endgroup$ Commented Jan 7, 2021 at 15:34
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    $\begingroup$ I can make this a windowed statistic if a suggested method to forecast requires that kind of input. I have login and session info which I use to generate this table $\endgroup$ Commented Jan 7, 2021 at 15:36

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