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.

翻译：本文介绍Sparklen，一个用于霍克斯过程的Python统计学习工具包，旨在兼顾效率与易用性。该软件包旨在为Python社区提供一套完整的尖端工具，专门用于指数型霍克斯过程的研究，尤其侧重于高维框架。它包含最先进的估计工具（内置支持正则化技术）以及新颖的分类方法。为提升计算性能，Sparklen采用高性能C++内核处理密集型任务。这种双语言架构使Sparklen成为应对计算密集型实际应用的有力解决方案。本文阐述其实现框架并提供示例，展示其功能与实际应用方法。