hawkeslib started with the ambition of having a simple Python implementation
of plain-vanilla Hawkes (or self-exciting processes), i.e. those
with factorized triggering kernels with exponential decay functions.
The docs contain tutorials, examples and a detailed API reference.
For other examples, see the examples/ folder.
The following models are available:
- Univariate Hawkes Process (with exponential delay)
- Bayesian Univariate Hawkes Process (with exponential delay)
- Poisson Process
- 'Bayesian' Poisson process
Bayesian variants implement methods for sampling from the posterior as well as calculating marginal likelihood (e.g. for Bayesian model comparison).
Cython (>=0.28) and numpy (>=1.14) and scipy must be installed prior to the installation as
they are required for the build.
$ pip install -U Cython numpy scipy
$ pip install hawkeslib
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