This paper introduces Sparklen, a statistical learning toolkit for Hawkes processes in Python, designed to bring together efficiency and ease of use. The purpose of this package is to provide the Python community with a complete suite of cutting-edge tools specifically tailored for the study of exponential Hawkes processes, with a particular focus on highdimensional framework. It includes state-of-the-art estimation tools with built-in support for incorporating regularization techniques, and novel classification methods. To enhance computational performance, Sparklen leverages a high-performance C++ core for intensive tasks. This dual-language approach makes Sparklen a powerful solution for computationally demanding real-world applications. Here, we present its implementation framework and provide illustrative examples, demonstrating its capabilities and practical usage.
View on arXiv@article{lacoste2025_2502.18979,
title={ Sparklen: A Statistical Learning Toolkit for High-Dimensional Hawkes Processes in Python },
author={ Romain Edmond Lacoste },
journal={arXiv preprint arXiv:2502.18979},
year={ 2025 }
}