Abstract
This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fréchet statistic of a network. The method splits the point process into overlapping windows, estimates kernel matrices in each window, and reconstructs the signed Laplacians by treating the kernel matrices as the adjacency matrices of the causal network. We demonstrate the effectiveness of our method through experiments on both simulated and cryptocurrency datasets. Our results show that our method is capable of accurately detecting and characterizing changes in the causal structure of multivariate Hawkes processes, and may have potential applications in fields such as finance and neuroscience. The proposed method is an extension of previous work on Fréchet statistics in point process settings and represents an important contribution to the field of change point detection in multivariate point processes. © 2024 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 63rd Conference on Decision and Control (CDC) |
| Publisher | IEEE |
| Pages | 2423-2428 |
| ISBN (Electronic) | 979-8-3503-1632-2, 9798350316339 |
| ISBN (Print) | 979-8-3503-1634-6 |
| DOIs | |
| Publication status | Published - Dec 2024 |
| Event | 63rd IEEE Conference on Decision and Control (CDC 2024) - Allianz MiCo, Milan Convention Centre, Milan, Italy Duration: 16 Dec 2024 → 19 Dec 2024 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (Electronic) | 2576-2370 |
Conference
| Conference | 63rd IEEE Conference on Decision and Control (CDC 2024) |
|---|---|
| Country/Territory | Italy |
| City | Milan |
| Period | 16/12/24 → 19/12/24 |
Funding
This work was supported in part by the U. S. Army Research Office under grant W911NF-21-1-0093, the National Science Foundation under grant CCF-2112457, and City University of Hong Kong under grant 9610639.