romain-e-lacoste/sparklen
A statistical learning toolkit for high-dimensional Hawkes processes in Python
This toolkit helps quantitative analysts, data scientists, and researchers understand and predict sequences of events that influence each other. It takes event data—like stock trades, social media interactions, or seismic activity—and identifies underlying patterns and influences, helping you simulate future events or classify different types of event cascades. This is for professionals who need to model and analyze complex, self-exciting event streams.
Available on PyPI.
Use this if you need to analyze event data where past events increase the likelihood of future events, especially in situations with many different event types.
Not ideal if your event data is simple, low-dimensional, or if events are entirely independent of each other.
Stars
19
Forks
2
Language
Python
License
BSD-3-Clause
Category
Last pushed
Dec 05, 2025
Commits (30d)
0
Dependencies
8
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