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tick is a Python 3 module for statistical learning, with a particular emphasis on time-dependent modeling. It is distributed under the 3-Clause BSD license, see LICENSE.txt.

The project was started in 2016 by Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas and Søren Vinther Poulsen at the Datascience initiative of École Polytechnique, France. The list of contributors is available in

Quick description

tick is a machine learning library for Python 3. The focus is on statistical learning for time dependent systems, such as point processes. Tick features also tools for generalized linear models and a generic optimization toolbox. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for regularization. It comes also with inference and simulation tools intended for end-users who for example can easily:

  • Perform linear, logistic or Poisson regression
  • Simulate point Hawkes processes with standard or exotic kernels.
  • Infer Hawkes models with various assumptions on the kernels: exponential or sum of exponential kernels, linear combination of basis kernels, sparse interactions, etc.

A comprehensive list of examples can be found at

and the documentation is available at

The paper associated to this library has been published at

If you use tick in a scientific publication, we would appreciate citations.

intel logo

The tick library is released with the support of Intel®. It uses the Intel® Math Kernel Library (MKL) optimized for Intel® Xeon Phi™ and Intel® Xeon™ processors. tick runs efficiently on everything from desktop computers to powerful high-performance servers.

Use cases

tick is used for many industrial applications including:

  • A joint work with the French national social security (CNAMTS) to analyses a huge health-care database, that describes the medical care provided to most of the French citizens. For this project, tick is used to detect weak signals in pharmacovigilance, in order quantify the impact of drugs exposures to the occurrence of adverse events.

  • High-frequency order book modeling in finance, in order to understand the interactions between different event types and/or between different assets, leveraging the full time resolution available in the original data.

  • Analyze the propagation of information in social media. Thanks to a dataset collected during 2017's presidential French election campaign on Twitter, tick is used to recover, for each topic, the network across which information spreads inside the political sphere.

Quick setup


tick currently works on Linux/OSX (Windows is experimental) systems and requires Python 3.5 or newer. Please have the required Python dependencies in your Python environment:

Install using pip

tick is available via pip. In your local Python environment (or global, with sudo rights), do:

pip install tick

Installation may take a few minutes to build and link C++ extensions. At this point tick should be ready to use available (if necessary, you can add tick to the PYTHONPATH as explained below).

Verify install

Run the following command and there should be no error

python3 -c "import tick;"

Source Installation

Please see the INSTALL document

Help and Support


Documentation is available on

This documentation is built with Sphinx and can be compiled and used locally by running make html from within the doc directory. This obviously needs to have Sphinx installed. Several tutorials and code-samples are available in the documentation.


To reach the developers of tick, please join our community channel on Gitter (

If you've found a bug that needs attention, please raise an issue here on Github. Please try to be as precise in the bug description as possible, such that the developers and other contributors can address the issue efficiently.


If you use tick in a scientific publication, we would appreciate citations. You can use the following bibtex entry:

  author = {{Bacry}, E. and {Bompaire}, M. and {Ga{\"i}ffas}, S. and {Poulsen}, S.},
  title = "{tick: a Python library for statistical learning, with
    a particular emphasis on time-dependent modeling}",
  journal = {ArXiv e-prints},
  eprint = {1707.03003},
  year = 2017,
  month = jul


Please see the CONTRIBUTING document