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).
Installation
Cython (>=0.28) and numpy (>=1.14) and scipy must be installed prior to the installation as
they are required for the build.
