Detecting Structural Shifts in Multivariate Hawkes Processes with Fr\'echet Statistics

TL;DR

This paper introduces a novel change point detection method for multivariate Hawkes processes using Fréchet statistics to analyze shifts in the underlying causal network structure, validated on simulated and real datasets.

Contribution

It extends Fréchet statistics to multivariate Hawkes processes for effective change point detection and causal network analysis.

Findings

Accurately detects change points in simulated data

Effectively characterizes causal structure shifts in cryptocurrency data

Demonstrates potential applications in finance and neuroscience

Abstract

This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fr\'echet 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 Fr\'echet statistics in point process settings and represents an important contribution to the field of change point detection in multivariate point processes.