THAP: A Matlab Toolkit for Learning with Hawkes Processes
This toolkit provides a practical resource for researchers and educators in statistics and computer science working with Hawkes processes, but it is incremental as it compiles existing methods rather than introducing new ones.
The authors tackled the need for accessible tools for analyzing event sequences by developing THAP, an open-source Matlab toolkit that implements both classic and state-of-the-art learning algorithms for Hawkes processes and their variants, making it beneficial for academic education and research.
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields. Among various point process models, Hawkes process and its variants attract many researchers in statistics and computer science these years because they capture the self- and mutually-triggering patterns between different events in complicated sequences explicitly and quantitatively and are broadly applicable to many practical problems. In this paper, we describe an open-source toolkit implementing many learning algorithms and analysis tools for Hawkes process model and its variants. Our toolkit systematically summarizes recent state-of-the-art algorithms as well as most classic algorithms of Hawkes processes, which is beneficial for both academical education and research. Source code can be downloaded from https://github.com/HongtengXu/Hawkes-Process-Toolkit.