Introduction

About hawkeslib

hawkeslib started with the ambition of presenting easy-to-use, well maintained implementations of plain-vanilla Hawkes (self-exciting) processes [1] [2], a form of evolutionary temporal point processes that is increasingly put to use in a variety of domains.

Some features of the library are

• Fast. most algorithms are implemented in Cython, cutting away most of the Python overhead for likelihood computations and parameter estimation algorithms that require scanning the data.
• Easy-to-use. Models implement a familiar, common interface.
• Good for beginners. The library implements a variety of plain vanilla self-exciting processes such as univariate and multivariate Hawkes processes with exponential delay densities, Poisson processes, and related Bayesian inference machinery.

In the future, we hope to add several extended models.

Installation

Cython (>=0.28) and numpy (>=1.14) must be installed prior to the installation.

$ pip install -U Cython numpy
$ pip install hawkeslib

Currently, the library only supports python 2.7.

Getting Started

The examples/ folder contains Jupyter notebooks that demonstrate basic use cases, in addition to the tutorial and example provided in these docs.

References

[1]	Hawkes, Alan G. “Point spectra of some mutually exciting point processes.” Journal of the Royal Statistical Society. Series B (Methodological) (1971): 438-443.

[2]	Bacry, Emmanuel, Iacopo Mastromatteo, and Jean-François Muzy. “Hawkes processes in finance.” Market Microstructure and Liquidity 1.01 (2015): 1550005.