Modeling Time-series using Burst Analysis

As a historian, I work a lot with time-series data. And I’m always looking for methods from time series analysis to model my data in new ways. A while ago I came across Kleinberg’s burst algorithm, which is a great method for finding ‘trending topics’ in historical textual data. Although it can also be applied to other signals. This blog post by Nikki Marinsek provides a nice introduction to the algorithm.


Looks cool! Do you have any recommendations on setting the parameter s? Or is that a matter of trial and error?

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It depends very much on the data, and the resolution of the time steps you are using, so in my experience it’s mostly trial and error.

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