Sparklen: A Statistical Learning Toolkit for High-Dimensional Hawkes Processes in Python

TL;DR

Sparklen is a Python toolkit that combines efficient C++ core and advanced statistical methods to facilitate high-dimensional Hawkes process analysis, including estimation, regularization, and classification.

Contribution

It introduces a comprehensive, high-performance Python package for high-dimensional Hawkes processes, integrating state-of-the-art estimation, regularization, and classification tools.

Findings

Demonstrates practical application through illustrative examples

Provides efficient computational performance for high-dimensional data

Includes novel classification methods for Hawkes processes

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.