# Hypergraphs on high dimensional time series sets using signature transform

arXiv: https://arxiv.org/abs/2507.15802 · HTML: https://mlsanity.com/paper/2507.15802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Signature Methods in Machine Learning](https://mlsanity.com/paper/2206.14674/) (2022)
- [SLOPE - Adaptive variable selection via convex optimization](https://mlsanity.com/paper/1407.3824/) (2014)
- [Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia](https://mlsanity.com/paper/1809.08504/) (2018)
- [Sparse Graphical Modelling via the Sorted L1-Norm](https://mlsanity.com/paper/2204.10403/) (2022)

## Abstract (arXiv)

In recent decades, hypergraphs and their analysis through Topological Data Analysis (TDA) have emerged as powerful tools for understanding complex data structures. Various methods have been developed to construct hypergraphs -- referred to as simplicial complexes in the TDA framework -- over datasets, enabling the formation of edges between more than two vertices. This paper addresses the challenge of constructing hypergraphs from collections of multivariate time series. While prior work has focused on the case of a single multivariate time series, we extend this framework to handle collections of such time series. Our approach generalizes the method proposed in Chretien and al. by leveraging the properties of signature transforms to introduce controlled randomness, thereby enhancing the robustness of the construction process. We validate our method on synthetic datasets and present promising results.