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I have a series of timestamps that represent the time a user clicked a certain button.

My goal is to detect the automated clicks, so I need to find recurring patterns in the data that may point to an automated script. The majority of the data is by regular users. I don't need to detect them online, I just need to detect them in historical data. I also have user_id to make a distinction between users.

Is there a default approach to detecting recurring patterns in a largely unstructured dataset?

Most things I've found are about anomaly detection on a certain value paired with a timestamp but my problem is not really anomaly-detection as I need to find patterns amongst unpatterned data

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  • $\begingroup$ Welcome to the site! Do you need to detect it as it happens? Are there multiple buttons you need to track? This is an anomaly detection problem. $\endgroup$ – Emre Aug 21 '17 at 8:47
  • $\begingroup$ I don't need to detect it as it happens, I just need to detect it in historical data. It's one event/button I need to track. $\endgroup$ – Deb Aug 21 '17 at 8:48
  • $\begingroup$ Do you also have some training data that distinguishes bots from users? $\endgroup$ – Emre Aug 21 '17 at 8:56
  • $\begingroup$ @Emre Unfortunately not $\endgroup$ – Deb Aug 21 '17 at 8:59
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The question can be super difficult if you only have the number of clicks per time-stamp. The reason is that you might find many different recurrent patterns! For instance certain time periods in year, month or day (according to the functionality of website) may attract a certain temporal pattern of clicks. But if you have more relevant features the story is different.

After all I will point out some directions and hope it helps:

Autocorrelation may initially give you a good insight about what is happening there.

Then go for Time-Warping methods to define similarity function and seek it along time-series.

Embedding in high-dimensional spaces disclose the temporal recurrent pattern of a time-series. It's mostly from physics literature and is called State-Space Recunstruction. This method transfers your time-series into a m-dimensional space using this and there you can see the time-series as a dynamic system which may help in detecting recurrent dynamics.

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  • $\begingroup$ Thank you for the info, I will checkout the links! Other relevant data I have are user id's so I am sure to only find patterns from a single user/bot. $\endgroup$ – Deb Aug 21 '17 at 9:27

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