Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Lacoste, Romain Edmond
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415269830
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