A0913
Title: Deep learning of point processes for modeling high-frequency data
Authors: Yoshihiro Gyotoku - University of Tokyo (Japan)
Ioane Muni Toke - CentraleSupelec (France)
Nakahiro Yoshida - University of Tokyo (Japan) [presenting]
Abstract: Applications of deep neural networks are investigated to a point process having an intensity with mixing covariates processes as input. The generic model includes Cox-type models and marked point processes as well as multivariate point processes. An oracle inequality and a rate of convergence are derived for the prediction error.
