Zobrazeno 1 - 10
of 30 982
pro vyhledávání: '"P. Hawkes"'
Networks representation aims to encode vertices into a low-dimensional space, while preserving the original network structures and properties. Most existing methods focus on static network structure without considering temporal dynamics. However, in
Externí odkaz:
http://arxiv.org/abs/2410.20627
We define a new model using a Hawkes process as a subordinator in a standard Brownian motion. We demonstrate that this Hawkes subordinated Brownian motion or more succinctly, variance-Hawkes process can be fit to 2018 and 2019 natural gas and crude o
Externí odkaz:
http://arxiv.org/abs/2410.08420
The Hawkes model is a past-dependent point process, widely used in various fields for modeling temporal clustering of events. Extending this framework, the multidimensional marked Hawkes process incorporates multiple interacting event types and addit
Externí odkaz:
http://arxiv.org/abs/2410.05008
Autor:
Liu, Qi, Liu, Yanchen, Li, Ruifeng, Cao, Chenhong, Li, Yufeng, Li, Xingyu, Wang, Peng, Feng, Runhan
The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to po
Externí odkaz:
http://arxiv.org/abs/2411.10258
Autor:
MacLaurin, James
We determine the large size limit of a network of interacting Hawkes Processes on an adaptive network. The flipping of the node variables is taken to have an intensity given by the mean-field of the afferent edges and nodes. The flipping of the edge
Externí odkaz:
http://arxiv.org/abs/2411.09260
Autor:
Bonnet, Anna, Sangnier, Maxime
This paper addresses nonparametric estimation of nonlinear multivariate Hawkes processes, where the interaction functions are assumed to lie in a reproducing kernel Hilbert space (RKHS). Motivated by applications in neuroscience, the model allows com
Externí odkaz:
http://arxiv.org/abs/2411.00621
Autor:
Avitabile, Daniele, MacLaurin, James
We prove a Large Deviation Principle for Hawkes Processes on sparse large disordered networks with a graphon structure. We apply our results to a stochastic SIS epidemiological model on a disordered networks, and determine Euler-Lagrange equations th
Externí odkaz:
http://arxiv.org/abs/2410.13682
The Bayesian two-step change point detection method is popular for the Hawkes process due to its simplicity and intuitiveness. However, the non-conjugacy between the point process likelihood and the prior requires most existing Bayesian two-step chan
Externí odkaz:
http://arxiv.org/abs/2409.17591
We introduce a spatiotemporal self-exciting point process $(N_t(x))$, boundedly finite both over time $[0,\infty)$ and space $\mathscr X$, with excitation structure determined by a graphon $W$ on $\mathscr X^2$. This graphon Hawkes process generalize
Externí odkaz:
http://arxiv.org/abs/2409.16903
Publikováno v:
Statistics & Probability Letters, Volume 214, 2024, 110192
The discrete-time Hawkes process (DTHP) is a sub-class of $g$-functions that serves as a discrete-time version of the continuous-time Hawkes process (CTHP). Like the CTHP, the DTHP also has the self-exciting property and its intensity depends on the
Externí odkaz:
http://arxiv.org/abs/2409.14405