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

arXiv: https://arxiv.org/abs/2502.18979 · HTML: https://mlsanity.com/paper/2502.18979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [AdaGrad stepsizes: Sharp convergence over nonconvex landscapes](https://mlsanity.com/paper/1806.01811/) (2018)
- [Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions](https://mlsanity.com/paper/2305.18471/) (2023)
- [Uncovering Causality from Multivariate Hawkes Integrated Cumulants](https://mlsanity.com/paper/1607.06333/) (2016)
- [Sparse and low-rank multivariate Hawkes processes](https://mlsanity.com/paper/1501.00725/) (2015)
- [Inference of multivariate exponential Hawkes processes with inhibition and application to neuronal activity](https://mlsanity.com/paper/2205.04107/) (2022)
- [Neuronal Network Inference and Membrane Potential Model using Multivariate Hawkes Processes](https://mlsanity.com/paper/2108.00758/) (2021)
- [FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels](https://mlsanity.com/paper/2210.04635/) (2022)
- [Parameter-free projected gradient descent](https://mlsanity.com/paper/2305.19605/) (2023)

## Abstract (arXiv)

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.