Work

Deep learning of point processes for modeling high-frequency data

Year: 2025

Type: preprint

Abstract: We investigate applications of deep neural networks to a point process having an intensity with mixing covariates processes as input. Our 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. A simulation study shows that the marked point process can be superior to the simple multivariate model in prediction. We apply the marked ratio model to real limit order book data

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2504.15944

Authors: Gyotoku, Yoshihiro, Toke, Ioane Muni, Yoshida, Nakahiro

Institutions: Japan Science and Technology Agency, The University of Tokyo, Mathématiques et Informatique pour la Complexité et les Systèmes

Language: English

Topic: Point processes and geometric inequalities

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Japan Society for the Promotion of Science, Japan Science and Technology Agency, Core Research for Evolutional Science and Technology

Awards: G2413678681, G2521286739, G4325941259, 大規模時空間従属性データ科学へ向けた先端的確率統計学の新展開, G6718509927, G7939374355, G8160764519

OpenAlex ID: W4414634007

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